StatisticalAnalysis
Matilde Correia
2024-05-07
#Required packages
if(!require(readxl)){install.packages("readxl"); library(readxl)}## Loading required package: readxlif(!require(correlation)){install.packages("correlation"); library(correlation)}## Loading required package: correlationif(!require(corrplot)){install.packages("corrplot"); library(corrplot)}## Loading required package: corrplot## corrplot 0.92 loadedif(!require(car)){install.packages("car"); library(car)}## Loading required package: car## Loading required package: carDataif(!require(mgcv)){install.packages("mgcv"); library(mgcv)}## Loading required package: mgcv## Loading required package: nlme## This is mgcv 1.9-0. For overview type 'help("mgcv-package")'.if(!require(rpart)){install.packages("rpart"); library(rpart)}## Loading required package: rpartif(!require(ggplot2)){install.packages("ggplot2"); library(ggplot2)}## Loading required package: ggplot2if(!require(gridExtra)){install.packages("gridExtra"); library(gridExtra)}## Loading required package: gridExtraif(!require(lme4)){install.packages("lme4"); library(lme4)}## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'lme4'## The following object is masked from 'package:nlme':
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## lmListif(!require(Matrix)){install.packages("Matrix"); library(Matrix)}
if(!require(lmtest)){install.packages("lmtest"); library(lmtest)}## Loading required package: lmtest## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numericif(!require(gamm4)){install.packages("gamm4"); library(gamm4)}## Loading required package: gamm4## This is gamm4 0.2-6if(!require(sjPlot)){install.packages("sjPlot"); library(sjPlot)}## Loading required package: sjPlotif(!require(sjmisc)){install.packages("sjmisc"); library(sjmisc)}## Loading required package: sjmiscif(!require(sjlabelled)){install.packages("sjlabelled"); library(sjlabelled)}## Loading required package: sjlabelled##
## Attaching package: 'sjlabelled'## The following object is masked from 'package:ggplot2':
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## as_labelif(!require(performance)){install.packages("performance"); library(performance)}## Loading required package: performanceif(!require(flexmix)){install.packages("flexmix"); library(flexmix)}## Loading required package: flexmix## Loading required package: latticeif(!require(glmmTMB)){install.packages("glmmTMB"); library(glmmTMB)}## Loading required package: glmmTMB## Warning in checkDepPackageVersion(dep_pkg = "TMB"): Package version inconsistency detected.
## glmmTMB was built with TMB version 1.9.10
## Current TMB version is 1.9.11
## Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)##
## Attaching package: 'glmmTMB'## The following object is masked from 'package:flexmix':
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## refitif(!require(DHARMa)){install.packages("DHARMa"); library(DHARMa)}## Loading required package: DHARMa## This is DHARMa 0.4.6. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')Import Dataset
#Import data
week_kuds <- read.csv("week_kuds2.2.csv", sep=";") #has the wrong week values
week_kuds3 <- read.csv("week_kuds1 - usar.csv", sep=";") #has the right week values
week_kuds$Week <- week_kuds3$Week #substitute the wrong for the right week in the dataset
#create week variable without the year
names(week_kuds)[names(week_kuds) == "Week"] <- "WeekYear"
week_kuds$Week <- substr(week_kuds$WeekYear, 1, 2)
#create year variable without the week
week_kuds$Year <- substr(week_kuds$WeekYear, 4, 7)
week_kuds$File <- as.factor(week_kuds$File)
week_kuds$Species <- as.factor(week_kuds$Species)
week_kuds$Transmitter <- as.factor(week_kuds$Transmitter)
week_kuds$KUD50 <- as.numeric(week_kuds$KUD50)
week_kuds$KUD95 <- as.numeric(week_kuds$KUD95)
week_kuds$Habitat <- as.factor(week_kuds$Habitat)
week_kuds$Migration <- as.factor(week_kuds$Migration)
week_kuds$ComImport <- as.factor(week_kuds$ComImport)
week_kuds$Length_cm <- as.numeric(week_kuds$Length_cm)
week_kuds$LengthStd <- as.numeric(week_kuds$LengthStd)
week_kuds$BodyMass <- as.numeric(week_kuds$BodyMass)
week_kuds$BodyMassStd <- as.numeric(week_kuds$BodyMassStd)
week_kuds$Longevity <- as.numeric(week_kuds$Longevity)
week_kuds$Vulnerability <- as.numeric(week_kuds$Vulnerability)
week_kuds$Troph <- as.numeric(week_kuds$Troph)
week_kuds$ReceiverDensity <- as.numeric(week_kuds$ReceiverDensity)
week_kuds$MonitArea_km2 <- as.numeric(week_kuds$MonitArea_km2)
week_kuds$MCP_km2 <- as.numeric(week_kuds$MCP_km2)
week_kuds$NReceivers <- as.numeric(week_kuds$NReceivers)
week_kuds$MaxDistReceivers <- as.numeric(week_kuds$MaxDistReceivers)
week_kuds$MaxLength <- as.numeric(week_kuds$MaxLength)
week_kuds$MaxBodyMass <- as.numeric(week_kuds$MaxBodyMass)
week_kuds$a <- as.numeric(week_kuds$a)
week_kuds$b <- as.numeric(week_kuds$b)
week_kuds$Week <- as.factor(week_kuds$Week)
week_kuds$Year <- as.factor(week_kuds$Year)
week_kuds$Spawn <- as.factor(week_kuds$Spawn)summary(week_kuds)## File Species Transmitter
## Seriola_rivoliana : 2787 Dsar :3708 A69-1008-104 : 284
## Epinephelus_marginatus1: 2055 Gmor :3170 A69-1008-102 : 281
## Gadus_morhua1 : 1635 Emar :3012 A69-1008-103 : 264
## Pseudocaranx_dentex : 1527 Sriv :2787 A69-1008-100 : 261
## Diplodus_sargus4 : 1474 Svir :1856 A69-1303-6685: 252
## Sphyraena_viridensis1 : 1298 Pden :1527 A69-1008-101 : 247
## (Other) :14836 (Other):9552 (Other) :24023
## KUD50 KUD95 WeekYear Habitat
## Min. :0.1620 Min. : 0.760 Length:25612 benthopelagic : 9100
## 1st Qu.:0.1640 1st Qu.: 0.762 Class :character demersal :11326
## Median :0.1730 Median : 0.818 Mode :character pelagic-neritic: 5186
## Mean :0.2267 Mean : 1.124
## 3rd Qu.:0.2390 3rd Qu.: 1.188
## Max. :3.2780 Max. :14.723
##
## Migration ComImport Length_cm LengthStd
## non-migratory:14095 high :15837 Min. : 12.20 Min. :0.1375
## oceanodromous:11517 medium: 9572 1st Qu.: 33.00 1st Qu.:0.3474
## minor : 203 Median : 46.50 Median :0.4926
## Mean : 49.95 Mean :0.4853
## 3rd Qu.: 62.00 3rd Qu.:0.6000
## Max. :118.00 Max. :1.0200
##
## BodyMass BodyMassStd Longevity Vulnerability
## Min. : 19.7 Min. :0.001964 Min. : 8.00 Min. :35.90
## 1st Qu.: 523.1 1st Qu.:0.049854 1st Qu.:13.50 1st Qu.:63.40
## Median : 1134.0 Median :0.109527 Median :22.70 Median :66.30
## Mean : 2436.8 Mean :0.172517 Mean :24.81 Mean :64.47
## 3rd Qu.: 2829.9 3rd Qu.:0.228268 3rd Qu.:29.00 3rd Qu.:71.50
## Max. :22826.3 Max. :1.490344 Max. :60.00 Max. :90.00
##
## Troph ReceiverDensity MonitArea_km2 MCP_km2
## Min. :2.860 Min. : 0.030 Min. : 0.690 Min. : 0.1447
## 1st Qu.:3.470 1st Qu.: 0.130 1st Qu.: 1.500 1st Qu.: 2.0148
## Median :4.090 Median : 0.330 Median : 7.640 Median : 12.2923
## Mean :3.935 Mean : 8.943 Mean : 6.752 Mean : 259.2622
## 3rd Qu.:4.310 3rd Qu.: 12.410 3rd Qu.: 8.380 3rd Qu.: 355.9882
## Max. :4.660 Max. :131.310 Max. :15.620 Max. :2990.2885
##
## NReceivers MaxDistReceivers MaxLength MaxBodyMass
## Min. : 4.00 Min. : 0.53 Min. : 38.0 Min. : 585
## 1st Qu.:19.00 1st Qu.: 3.21 1st Qu.: 65.9 1st Qu.: 3000
## Median :45.00 Median : 9.25 Median :122.0 Median :14300
## Mean :43.15 Mean :34.78 Mean :116.1 Mean :31655
## 3rd Qu.:55.00 3rd Qu.:74.03 3rd Qu.:150.0 3rd Qu.:60000
## Max. :98.00 Max. :87.39 Max. :200.0 Max. :96000
##
## a b SpawnSeason Tp
## Min. :0.00391 Min. :2.801 Length:25612 Min. : 2.0
## 1st Qu.:0.00900 1st Qu.:2.880 Class :character 1st Qu.: 292.0
## Median :0.01010 Median :3.060 Mode :character Median : 418.0
## Mean :0.01437 Mean :3.021 Mean : 731.2
## 3rd Qu.:0.01933 3rd Qu.:3.120 3rd Qu.:1465.0
## Max. :0.09550 Max. :3.280 Max. :2413.0
##
## RI ROM_mh Week Year Spawn
## Min. :0.0000 Min. : 0.00 38 : 572 2008 :4050 A : 1335
## 1st Qu.:0.6300 1st Qu.: 8.40 37 : 568 2011 :3583 SS:22642
## Median :0.8100 Median : 21.20 39 : 556 2021 :3128 W : 1635
## Mean :0.7518 Mean : 38.03 35 : 548 2009 :2073
## 3rd Qu.:0.9400 3rd Qu.: 45.40 30 : 546 2007 :1906
## Max. :1.0000 Max. :708.30 24 : 545 2010 :1501
## (Other):22277 (Other):9371#Number of individuals by species
num_transmissores <- aggregate(Transmitter ~ Species, data = week_kuds, FUN = function(x) length(unique(x)))
barplot(num_transmissores$Transmitter,
names.arg = num_transmissores$Species,
xlab = "Species",
ylab = "Number of Individuals",
col = "deepskyblue",
border = "black",
ylim= c(0,180),
yaxp = c(0, 180, 3),
cex.names = 0.8,
las = 2)
text(x = barplot(num_transmissores$Transmitter, plot = FALSE),
y = num_transmissores$Transmitter,
label = num_transmissores$Transmitter,
pos = 3,
cex = 0.7)
#Number of observations by species
contagem_observacoes <- table( week_kuds$File, week_kuds$Species)
barplot(contagem_observacoes,
xlab = "Species",
ylab = "Number of Observations",
col= c("deepskyblue"),
border = "black",
ylim = c(0, 4000),
yaxp = c(0, 4000, 8),
cex.names = 0.8,
las = 2)
ggplot(week_kuds, aes(LengthStd, KUD95, color=File)) +
geom_point() +
labs(title = "KUD95 ~ Length por File")
ggplot(week_kuds, aes(BodyMassStd, KUD95, color=File)) +
geom_point() +
labs(title = "KUD95 ~ BodyMass por File")
ggplot(week_kuds, aes(Vulnerability, KUD95, color=File)) +
geom_point() +
labs(title = "KUD95 ~ Vulnerability por File")
ggplot(week_kuds, aes(Longevity, KUD95, color=File)) +
geom_point() +
labs(title = "KUD95 ~ Longevity por File")
ggplot(week_kuds, aes(Troph, KUD95, color=File)) +
geom_point() +
labs(title = "KUD95 ~ Troph por File")
ggplot(week_kuds, aes(LengthStd, KUD95)) +
geom_point() +
facet_wrap(~File, ncol=7) +
labs(title = "KUD95 ~ Length por File")
Linear Model Assumptions
Normality Assumption
KUD95
summary(week_kuds$KUD95)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.760 0.762 0.818 1.124 1.188 14.723hist(week_kuds$KUD95, col= "deepskyblue", main = "KUD95 Frequency", xlab="KUD95", breaks = 30)
qqnorm(week_kuds$KUD95)
qqline(week_kuds$KUD95, col = 2)
boxplot(week_kuds$KUD95, col="deepskyblue", ylab = "KUD95", main = "Boxplot of KUD95")
KUD50
summary(week_kuds$KUD50)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1620 0.1640 0.1730 0.2267 0.2390 3.2780hist(week_kuds$KUD50, col= "green2", main = "KUD50 Frequency", xlab="KUD50", breaks = 30)
qqnorm(week_kuds$KUD50)
qqline(week_kuds$KUD50, col = 2)
boxplot(week_kuds$KUD50, col= "green2", ylab = "KUD50", main = "Boxplot of KUD50")
First of all, we saw the summary of KUD95 and KUD50, to have a better perception of the dimension of these variables. After, we investigated if this variables were Normal distributed. For that we used two methods: Histogram and Q-Q plot. If a variable is Normal distributed, it is expected to see the shape of a bell in the histogram, symmetrical around an average. In addition, in the Q-Q plot, it’s expected to see the points distributed along the reference line, without major deviations. With this said, we could see that the histograms don’t have the shape of a bell, instead they only have one side of the bell, so the points are not distributed symmetrically around an average, and the points from the Q-Q plot are very distant from the reference line. This indicate that our response variables are not Normal distributed. We also made a boxplot and as it’s possible to identify a bunch of outliers, it is further proof that the response variable doesn’t follow a Normal distribuition, otherwise the points would be more contained inside the box. The Normality assumption is not met for both response variables. The data violates the Normality Assumption.
Outliers Analysis
#First removal
boxplot.stats(week_kuds$KUD95) #superior limit where outliers start is 1.827## $stats
## [1] 0.760 0.762 0.818 1.188 1.827
##
## $n
## [1] 25612
##
## $conf
## [1] 0.8137942 0.8222058
##
## $out
## [1] 3.494 2.506 1.990 1.943 1.911 1.923 1.906 1.931 1.969 1.884
## [11] 1.871 1.851 1.842 1.923 1.977 2.198 2.102 2.254 2.059 1.993
## [21] 1.862 1.833 1.914 1.840 1.881 1.950 1.963 2.062 2.061 1.945
## [31] 2.114 1.912 1.959 1.854 1.887 1.829 2.110 2.254 1.921 2.071
## [41] 2.030 1.990 2.043 1.996 2.074 2.948 2.175 1.843 2.186 1.847
## [51] 1.833 1.917 1.830 1.848 1.880 1.862 1.834 1.940 2.013 1.880
## [61] 2.046 2.011 1.890 1.996 2.650 1.915 1.911 2.112 1.959 2.016
## [71] 1.840 2.583 2.018 2.291 2.184 2.684 2.598 1.863 1.850 2.007
## [81] 1.863 1.962 1.859 1.988 2.412 3.857 2.186 2.458 2.594 4.133
## [91] 3.133 2.087 2.145 1.829 2.255 2.222 2.122 2.226 2.227 2.248
## [101] 2.205 2.066 2.026 1.881 1.993 2.109 8.410 7.846 8.805 4.991
## [111] 3.834 6.338 8.343 6.595 7.410 7.560 6.803 7.530 5.109 5.559
## [121] 5.943 1.882 1.896 2.081 1.995 2.254 1.996 2.209 2.101 2.215
## [131] 2.192 2.050 1.932 2.043 2.179 1.910 2.239 1.964 2.132 2.088
## [141] 1.994 1.894 2.307 1.906 1.925 7.752 7.273 5.443 2.245 2.754
## [151] 2.823 3.655 7.074 7.297 2.410 2.241 1.845 2.282 2.091 2.135
## [161] 1.883 1.844 1.959 2.447 1.864 2.127 2.095 2.113 1.848 1.857
## [171] 2.114 2.345 2.211 2.835 3.964 4.869 2.149 2.740 1.866 2.017
## [181] 1.926 1.989 1.929 1.932 2.196 2.081 1.961 1.947 1.828 2.818
## [191] 2.257 2.018 2.932 3.674 2.695 3.017 2.218 2.412 3.188 3.432
## [201] 3.214 3.654 2.391 2.068 2.110 3.779 2.213 2.763 1.842 1.928
## [211] 2.254 3.663 3.694 3.016 2.175 3.733 2.219 2.174 2.984 2.957
## [221] 1.926 3.514 2.182 2.023 3.849 3.615 3.768 2.181 2.203 3.541
## [231] 3.713 3.418 2.157 2.634 2.261 2.330 1.978 2.478 2.204 3.226
## [241] 2.163 2.707 2.753 2.795 1.892 1.841 2.052 3.841 2.171 2.124
## [251] 2.346 1.919 2.019 1.877 2.716 4.662 1.844 2.007 1.950 2.564
## [261] 2.128 1.855 2.799 4.898 2.081 2.024 1.862 2.253 2.233 5.602
## [271] 1.994 1.996 1.949 3.564 3.282 3.306 2.570 2.866 3.495 3.759
## [281] 3.517 3.326 2.138 2.861 3.365 4.136 2.711 4.482 2.947 2.276
## [291] 3.196 1.902 4.300 1.976 2.350 2.811 2.580 2.143 2.310 2.124
## [301] 3.354 2.123 1.903 2.123 2.701 2.731 2.893 5.206 2.251 4.528
## [311] 3.570 2.272 2.751 2.619 4.502 2.775 4.483 5.689 2.514 7.286
## [321] 2.030 2.073 2.116 3.374 2.435 3.440 4.270 4.847 3.389 2.024
## [331] 3.062 2.459 2.213 3.317 1.918 4.377 6.218 1.832 3.935 3.502
## [341] 4.627 2.368 4.496 4.090 4.301 2.478 2.395 2.035 2.095 1.839
## [351] 2.505 2.816 4.097 2.090 1.905 1.850 2.109 2.213 3.372 3.291
## [361] 3.445 3.413 2.982 1.836 1.906 1.931 2.053 1.857 1.981 2.216
## [371] 2.483 6.556 3.120 2.911 6.831 7.292 1.931 3.749 2.091 2.894
## [381] 4.314 3.429 2.991 2.512 3.412 2.822 6.282 2.099 2.991 1.982
## [391] 1.894 1.848 3.602 1.951 2.512 1.926 3.774 3.666 3.127 2.247
## [401] 1.935 2.295 1.880 3.747 4.277 3.346 4.987 4.750 5.731 3.001
## [411] 2.046 2.365 3.716 3.190 4.881 3.413 1.846 1.848 1.920 1.957
## [421] 1.993 1.833 4.983 9.142 2.883 3.143 2.938 3.088 2.740 2.557
## [431] 2.253 1.901 1.959 2.011 1.960 5.388 6.075 4.080 7.662 9.166
## [441] 6.857 6.955 6.223 5.543 8.678 10.692 6.300 9.127 5.232 6.299
## [451] 4.754 14.723 5.417 13.452 3.026 13.020 2.517 7.581 4.883 6.159
## [461] 4.564 2.584 7.030 3.266 4.325 3.196 3.482 2.349 2.465 2.893
## [471] 2.364 2.563 2.204 2.349 3.029 2.432 1.833 1.877 2.304 1.937
## [481] 3.234 2.231 1.840 2.499 3.291 2.769 2.274 3.505 3.080 2.770
## [491] 3.856 2.325 2.550 1.968 5.251 4.289 6.615 2.360 3.621 3.010
## [501] 2.199 3.563 3.469 3.019 3.413 2.553 3.060 2.545 2.630 3.086
## [511] 3.400 4.225 3.862 3.509 2.972 2.876 3.812 3.649 7.300 3.578
## [521] 6.265 3.600 4.134 4.235 6.680 2.677 7.472 5.010 4.652 4.589
## [531] 2.969 3.581 3.344 2.251 2.843 3.097 3.553 5.515 2.485 1.927
## [541] 4.721 2.309 1.867 2.428 2.190 2.251 3.297 2.576 2.354 2.102
## [551] 2.673 2.101 2.331 3.647 3.661 2.210 4.367 3.398 4.053 3.764
## [561] 3.244 3.430 2.425 1.981 3.465 2.070 1.910 3.141 2.196 4.136
## [571] 1.959 3.651 2.527 2.989 1.907 2.204 2.283 1.883 12.571 6.403
## [581] 3.991 2.756 2.322 1.972 1.834 2.324 2.748 3.038 2.873 1.908
## [591] 1.941 1.897 2.058 2.150 1.846 1.956 1.932 1.967 1.973 2.202
## [601] 2.199 2.869 2.814 2.126 2.711 2.418 2.633 2.495 2.113 2.059
## [611] 2.080 1.955 2.544 2.072 1.905 2.230 2.271 2.002 2.174 2.089
## [621] 1.894 2.824 2.801 2.088 1.891 2.229 1.899 2.322 2.326 1.838
## [631] 1.956 2.102 1.884 2.007 1.877 1.844 2.365 2.365 2.038 1.855
## [641] 1.998 2.446 1.908 2.147 1.856 3.222 2.597 2.174 3.344 3.512
## [651] 2.297 1.941 1.927 2.037 1.844 1.952 2.321 2.022 1.898 2.127
## [661] 2.362 1.912 1.848 2.638 1.985 3.118 2.782 2.620 2.352 1.952
## [671] 2.056 2.113 3.690 2.928 1.860 1.863 3.208 3.019 2.057 1.943
## [681] 3.092 2.367 1.866 1.950 1.857 5.591 1.875 2.371 2.893 2.777
## [691] 3.497 3.529 2.143 1.964 2.037 2.069 1.904 1.860 6.832 8.015
## [701] 7.050 6.124 4.640 2.892 2.148 1.964 1.889 1.869 2.179 1.837
## [711] 2.038 1.919 2.025 2.226 1.894 1.995 1.882 2.219 1.966 2.157
## [721] 2.379 2.570 1.870 2.792 2.601 2.785 2.281 2.324 2.323 1.992
## [731] 1.904 2.449 1.937 3.336 2.001 1.858 1.949 2.110 1.830 2.316
## [741] 2.087 2.668 2.327 1.938 1.903 2.341 1.974 3.370 1.977 2.504
## [751] 3.531 1.960 2.544 1.857 1.946 2.263 2.161 1.833 1.950 1.871
## [761] 2.165 2.428 2.134 1.921 2.089 2.215 1.902 2.691 1.862 2.102
## [771] 2.278 2.009 1.898 2.003 1.925 2.487 2.063 1.882 2.525 2.043
## [781] 2.190 2.671 2.155 2.654 2.009 2.251 1.971 3.972 1.959 1.910
## [791] 1.935 4.348 2.294 2.326 2.714 2.672 2.258 2.491 4.375 2.072
## [801] 2.416 2.309 4.256 2.977 2.863 3.173 2.860 2.710 2.447 1.939
## [811] 2.774 2.038 1.926 1.945 1.988 2.082 2.113 2.174 3.307 2.694
## [821] 1.931 1.930 2.112 1.874 2.674 2.288 3.084 4.436 2.833 4.152
## [831] 2.977 2.133 4.327 5.747 5.744 6.663 4.367 4.265 3.975 3.942
## [841] 4.179 3.183 3.926 3.933 3.301 3.645 3.479 4.090 3.835 4.712
## [851] 4.228 4.228 1.839 4.087 3.290 2.275 1.971 2.487 4.031 2.499
## [861] 2.023 2.120 1.870 1.846 1.920 2.482 2.161 2.737 2.806 2.750
## [871] 2.399 1.973 2.005 2.066 2.205 1.833 1.840 2.108 1.950 2.569
## [881] 2.296 3.043 1.955 2.276 1.983 1.940 1.843 1.953 2.180 3.274
## [891] 2.332 2.058 4.353 8.238 2.898 5.960 3.831 7.223 2.179 2.172
## [901] 2.733 3.007 2.739 3.861 4.323 5.778 4.822 2.890 2.680 2.051
## [911] 2.812 2.180 5.758 2.142 3.706 6.758 4.337 11.107 1.856 3.869
## [921] 5.272 6.878 3.506 2.368 4.287 2.632 3.610 4.940 2.136 10.288
## [931] 5.504 3.825 2.962 8.253 2.152 2.014 3.858 3.813 2.646 6.501
## [941] 3.706 1.958 1.875 4.986 8.326 6.771 3.151 3.925 6.202 6.093
## [951] 3.201 5.867 2.927 3.934 3.625 4.717 2.414 5.743 2.253 3.027
## [961] 4.868 2.686 5.197 3.668 3.049 2.354 3.948 4.113 4.665 5.629
## [971] 5.294 2.669 5.755 3.243 2.908 6.710 8.595 6.606 3.964 4.516
## [981] 3.395 3.610 3.104 1.914 2.905 2.685 1.958 2.745 1.886 3.156
## [991] 3.407 5.436 5.951 3.141 4.263 4.465 3.914 3.212 2.939 1.916
## [1001] 2.279 2.051 2.236 1.892 4.095 2.290 1.982 4.089 2.096 2.075
## [1011] 3.820 3.376 2.338 2.371 3.153 2.001 3.050 2.861 1.892 1.832
## [1021] 2.000 2.546 3.286 2.777 3.636 2.140 1.938 2.053 2.797 4.270
## [1031] 6.197 3.766 3.937 3.400 2.105 2.117 1.982 1.912 1.951 2.383
## [1041] 2.418 2.177 1.982 2.304 1.886 3.224 2.928 3.258 3.273 2.824
## [1051] 2.260 2.377 4.008 7.483 10.194 7.092 8.171 7.961 2.512 8.048
## [1061] 6.367 5.661 5.727 2.974 3.568 3.919 6.383 2.240 2.364 2.690
## [1071] 2.251 1.835 3.220 2.057 2.515 2.494 6.026 5.446 2.549 2.477
## [1081] 2.150 1.834 2.661 2.407 1.896 2.424 1.941 1.906 1.867 2.387
## [1091] 2.354 1.926 1.834 2.310 1.862 1.859 1.915 3.120 2.501 2.035
## [1101] 1.899 2.789 2.249 2.491 3.089 3.157 1.990 1.840 1.938 2.064
## [1111] 1.940 3.729 1.977 2.219 1.883 3.160 2.179 1.907 1.856 3.524
## [1121] 5.260 2.735 5.987 2.045 5.331 2.187 6.991 5.009 2.530 4.542
## [1131] 2.980 2.813 2.493 2.162 1.904 2.087 1.849 2.477 2.840 2.536
## [1141] 2.294 3.693 3.082 3.394 2.465 2.180 3.043 3.703 3.192 3.673
## [1151] 3.130 3.101 3.422 2.900 3.472 3.299 2.863 3.122 2.281 2.662
## [1161] 2.926 2.280 1.909 1.971 3.102 2.304 6.410 2.913 2.238 3.631
## [1171] 3.439 3.186 3.703 4.658 5.929 3.439 2.347 2.731 4.574 1.970
## [1181] 2.832 5.992 2.267 1.977 3.533 2.102 2.501 2.856 2.883 2.716
## [1191] 2.779 2.438 2.111 2.869 2.911 2.269 3.349 3.366 2.444 1.902
## [1201] 2.239 2.001 2.994 1.862 2.735 2.434 2.538 3.063 2.024 2.292
## [1211] 2.982 3.153 3.020 6.871 3.692 3.801 2.259 1.919 2.386 2.142
## [1221] 3.859 3.014 1.838 1.845 1.967 1.914 2.005 1.902 1.901 1.922
## [1231] 1.942 2.046 2.167 2.132 2.442 2.140 2.281 2.184 2.361 2.280
## [1241] 2.179 2.039 1.840 2.814 2.307 2.654 1.861 3.452 2.322 3.316
## [1251] 2.201 3.653 2.260 3.723 2.778 4.404 2.534 5.732 2.261 2.365
## [1261] 2.593 2.678 2.650 3.306 1.907 4.226 3.440 2.742 3.663 6.081
## [1271] 2.802 4.764 1.959 3.817 3.287 5.968 5.266 2.224 2.787 4.480
## [1281] 7.055 5.270 5.713 5.303 3.681 4.717 4.598 2.848 2.966 3.109
## [1291] 3.156 2.149 2.399 2.170 2.131 1.948 2.439 2.239 2.078 2.055
## [1301] 1.990 2.532 2.691 2.643 2.512 2.457 3.262 2.176 2.457 2.831
## [1311] 1.989 2.813 2.521 5.745 5.690 2.718 5.975 8.509 7.192 5.954
## [1321] 2.408 1.884 2.303 2.081 2.249 2.181 2.177 2.069 2.241 1.970
## [1331] 1.962 2.166 1.979 2.315 2.551 2.226 2.924 3.231 3.726 2.565
## [1341] 4.009 3.730 2.471 2.132 2.102 2.178 2.066 2.401 2.106 2.309
## [1351] 2.154 2.369 1.878 2.413 2.318 2.137 1.941 2.042 3.060 2.166
## [1361] 3.072 2.008 3.622 2.164 3.535 2.312 3.368 2.348 3.537 2.827
## [1371] 4.070 2.231 2.893 1.854 2.252 3.087 2.584 3.095 2.072 4.020
## [1381] 4.407 4.442 4.619 3.443 7.920 9.410 1.980 2.305 3.841 2.924
## [1391] 3.970 3.106 6.181 5.258 3.770 8.613 3.881 2.934 2.011 3.122
## [1401] 1.884 2.828 4.406 2.369 2.604 2.510 2.178 2.037 2.058 2.074
## [1411] 2.160 2.075 1.978 2.756 2.973 3.319 3.690 3.336 3.755 2.869
## [1421] 2.280 3.990 5.722 3.290 5.035 3.311 5.308 2.129 2.087 2.243
## [1431] 1.983 1.984 2.127 2.633 2.665 2.179 2.992 3.018 2.211 1.843
## [1441] 5.192 1.953 3.112 1.884 2.699 9.000 8.350 2.638 3.236 2.681
## [1451] 4.854 3.304 3.152 2.870 2.058 7.328 2.193 2.184 2.098 2.215
## [1461] 2.111 1.920 2.273 1.941 1.944 2.131 2.139 2.278 2.942 2.283
## [1471] 1.920 1.985 1.835 2.255 1.987 3.135 1.931 2.576 1.902 2.239
## [1481] 2.182 1.950 2.313 2.120 1.864 2.419 2.256 1.828 2.019 1.895
## [1491] 2.140 1.846 2.106 1.953 1.834 2.236 2.171 2.256 1.913 1.850
## [1501] 3.027 2.218 2.290 2.032 2.181 1.878 1.896 2.384 2.134 2.279
## [1511] 1.941 2.400 2.278 1.988 2.274 2.705 1.944 2.351 2.342 1.958
## [1521] 1.886 2.794 2.263 2.169 2.056 2.267 2.176 1.938 1.929 2.170
## [1531] 2.852 2.239 2.031 2.272 2.199 1.894 2.116 2.411 2.157 1.974
## [1541] 2.290 1.996 2.256 2.155 2.115 2.120 2.194 2.232 1.862 2.180
## [1551] 2.582 2.145 1.970 2.186 2.167 1.847 2.275 1.972 1.984 2.075
## [1561] 1.941 1.900 2.459 1.965 1.860 2.142 1.993 1.936 1.925 1.960
## [1571] 2.246 2.264 2.437 1.897 2.211 2.753 1.969 2.554 4.832 3.098
## [1581] 2.500 1.902 3.463 3.322 2.462 4.689 2.104 3.951 1.916 5.188
## [1591] 9.283 5.285 7.480 5.268 4.734 2.401 6.784 7.548 8.372 7.651
## [1601] 3.549 2.562 3.470 4.713 3.940 4.456 2.341 2.084 2.041 2.859
## [1611] 2.585 2.550 2.396 2.935 2.560 2.506 2.977 3.008 2.307 2.109
## [1621] 2.921 2.733 4.751 3.289 2.159 1.947 3.015 1.915 2.112 1.964
## [1631] 2.690 2.701 2.051 2.335 1.938 2.169 2.958 2.479 2.556 2.553
## [1641] 1.994 2.595 2.454 2.907 3.825 3.501 3.662 2.434 2.087 1.935
## [1651] 2.410 11.112 3.289 3.364 2.305 3.743 2.620 4.056 14.143 2.270
## [1661] 1.969 2.286 2.230 1.947 2.661 3.824 2.356 3.138 1.922 1.903
## [1671] 2.543 2.018 2.163 2.507 3.497 3.223 1.850 2.221 2.281 1.937
## [1681] 1.903 2.011 2.030 2.222 2.025 1.828 2.229 1.910 2.058 2.415
## [1691] 2.496 2.582 2.549 3.221 3.198 2.659 2.393 6.273 2.560 2.291
## [1701] 3.591 1.873 2.307 1.937 1.934 2.646 3.347 2.052 2.665 3.201
## [1711] 2.609 2.043 2.165 2.171 2.022 2.569 2.163 2.233 2.049 1.927
## [1721] 2.208 4.605 3.304 2.901 1.943 2.601 2.368 1.868 2.747 1.943
## [1731] 2.052 2.483 2.226 7.170 2.647 3.872 5.113 2.936 4.079 2.179
## [1741] 2.477 1.868 2.635 1.960 2.145 2.212 2.686 2.038 2.137 2.010
## [1751] 2.391 2.464 1.999 1.863 1.840 2.308 1.849 2.171 3.566 5.018
## [1761] 2.881 2.006 1.873 1.869 1.890 1.864 2.140 2.259 1.885 2.351
## [1771] 2.108 2.487 2.155 2.126 3.545 3.784 2.508 1.966 1.880 1.833
## [1781] 1.907 2.437 2.673 2.132 1.843 2.303 2.015 2.051 2.386 1.955
## [1791] 1.895 1.838 1.832 2.230 2.149 2.604 2.135 2.264 1.892 1.863
## [1801] 2.135 1.852 2.282 1.900 1.922 2.072 2.015 1.955 1.938 2.179
## [1811] 2.046 2.070 2.151 2.185 2.105 2.263 2.123 2.071 2.054 2.187
## [1821] 1.833 1.990 1.925 1.928 1.837 2.977 2.249 3.306 2.323 2.206
## [1831] 1.871 1.829 2.283 2.336 2.027 2.280 2.356 2.029 2.218 2.260
## [1841] 2.626 2.313 3.677 2.748 1.906 2.214 2.158 2.021 1.933 2.127
## [1851] 2.056 2.055 2.818 2.479 2.090 2.766 2.030 1.897 2.065 2.632
## [1861] 1.992 2.011 2.409 1.927 1.870 2.296 1.906 1.842 1.907 2.175
## [1871] 2.606 2.086 1.848 1.903 1.912 2.878 1.880 1.884 2.170 1.985
## [1881] 2.194 2.162 1.982 1.995 2.050 2.282 1.924 2.134 2.100 2.212
## [1891] 2.244 2.230 2.283 2.161 1.964 1.893 2.134 1.863 1.936 1.936
## [1901] 2.198 2.070 2.981 1.949 2.122 1.923 2.027 1.973 3.191 3.424
## [1911] 3.158 3.116 3.439 2.335 2.040 1.842 1.980 2.270 3.220 2.252
## [1921] 2.905 2.041 2.809 3.249 3.040 2.005 2.242 6.419 2.480 3.608
## [1931] 2.917 7.595 2.294 4.346 2.149 2.994 2.159 5.007 4.290 5.729
## [1941] 6.758 3.881 4.552 2.657 3.757 7.285 4.808 3.424 4.868 2.899
## [1951] 3.431 3.986 6.578 2.155 2.754 1.850 2.488 2.237 2.340 2.265
## [1961] 2.243 2.335 2.407 1.828 2.474 1.890 2.221 2.110 2.284 2.179
## [1971] 2.415 2.181 2.111 2.252 2.011 2.093 2.073 2.205 2.050 3.258
## [1981] 2.664 2.077 2.387 1.894 4.319 3.300 2.872 4.088 2.790 3.351
## [1991] 1.925 4.061 9.722 6.436 7.040 7.093 6.753 7.974 11.149 5.494
## [2001] 4.417 7.775 3.139 7.882 6.483 5.957 6.503 3.114 2.082 2.318
## [2011] 2.262 2.070 2.683 2.112 1.988 2.609 2.168 2.096 2.234 2.241
## [2021] 2.334 1.862 2.165 1.979 1.999 2.323 2.208 2.038 2.108 1.986
## [2031] 1.862 2.244 3.032 3.194 5.279 4.739 2.027 4.129 2.561 3.269
## [2041] 4.189 2.681 1.993 2.791 3.215 4.315 5.551 5.477 4.364 2.829
## [2051] 3.659 3.144 8.605 2.874 2.364 2.821 2.116 7.618 4.648 8.875
## [2061] 7.401 7.650 3.922 5.944 2.072 2.242 2.465 2.078 1.872 2.071
## [2071] 1.988 1.831 2.185 2.166 1.864 1.887 2.045 2.290 2.097 2.167
## [2081] 1.959 2.725 2.360 2.683 7.266 2.602 2.552 9.255 3.083 6.517
## [2091] 5.857 4.672 8.100 1.992 2.044 4.335 2.551 3.069 3.020 2.032
## [2101] 2.029 11.975 3.568 2.704 2.313 9.652 2.231 4.648 5.858 6.964
## [2111] 4.225 3.395 2.763 2.698 5.374 2.130 4.621 3.821 5.233 6.187
## [2121] 6.514 4.849 4.905 3.598 5.273 5.458 4.593 6.036 2.021 1.870
## [2131] 1.888 2.182 1.909 1.909 2.105 2.827 2.746 2.255 3.480 5.813
## [2141] 8.022 6.582 5.747 6.456 6.995 6.768 7.067 6.423 4.862 5.624
## [2151] 5.636 6.363 7.289 3.677 2.475 2.521 2.289 1.844 2.075 1.967
## [2161] 2.161 1.868 1.920 2.614 1.947 2.101 10.786 6.310 6.884 5.578
## [2171] 6.710 7.002 3.200 3.147 5.500 2.516 3.878 4.383 6.587 7.341
## [2181] 2.817 5.978 3.365 2.060 1.994 1.870 3.879 2.038 2.265 2.099
## [2191] 2.149 1.954 2.059 1.973 2.024 1.862 1.885 2.088 2.055 1.922
## [2201] 2.003 1.917 6.133 1.869 1.944 1.858 1.910 1.993 1.906 1.919
## [2211] 1.885 1.948 2.038 2.014 2.018 2.083 2.004 1.856 2.059 4.520
## [2221] 2.067 2.109 1.969 1.828 1.951 2.090 2.822 1.952 1.868 2.027
## [2231] 1.841 1.842 2.004 1.983 1.928 1.882 1.936 2.181rem_out1_kud95 <- subset(week_kuds, KUD95 < 1.827)
summary(rem_out1_kud95)## File Species Transmitter
## Seriola_rivoliana : 2676 Dsar :3608 A69-1008-104 : 283
## Epinephelus_marginatus1: 2053 Gmor :3041 A69-1008-102 : 281
## Gadus_morhua1 : 1555 Emar :2993 A69-1008-103 : 264
## Diplodus_sargus4 : 1469 Sriv :2676 A69-1008-100 : 261
## Pseudocaranx_dentex : 1305 Svir :1533 A69-1303-6685: 249
## Sphyraena_viridensis1 : 1263 Pden :1305 A69-1008-101 : 247
## (Other) :13051 (Other):8216 (Other) :21787
## KUD50 KUD95 WeekYear Habitat
## Min. :0.1620 Min. :0.7600 Length:23372 benthopelagic : 8090
## 1st Qu.:0.1640 1st Qu.:0.7620 Class :character demersal :10887
## Median :0.1690 Median :0.7935 Mode :character pelagic-neritic: 4395
## Mean :0.1984 Mean :0.9441
## 3rd Qu.:0.2130 3rd Qu.:1.0550
## Max. :0.5020 Max. :1.8260
##
## Migration ComImport Length_cm LengthStd
## non-migratory:13387 high :14579 Min. : 12.20 Min. :0.1375
## oceanodromous: 9985 medium: 8593 1st Qu.: 32.50 1st Qu.:0.3451
## minor : 200 Median : 45.50 Median :0.5000
## Mean : 49.65 Mean :0.4873
## 3rd Qu.: 62.00 3rd Qu.:0.6017
## Max. :118.00 Max. :1.0200
##
## BodyMass BodyMassStd Longevity Vulnerability
## Min. : 19.7 Min. :0.001964 Min. : 8.00 Min. :35.90
## 1st Qu.: 503.6 1st Qu.:0.051925 1st Qu.:13.50 1st Qu.:63.40
## Median : 1134.0 Median :0.118734 Median :22.70 Median :66.30
## Mean : 2479.0 Mean :0.176874 Mean :25.08 Mean :64.65
## 3rd Qu.: 2831.0 3rd Qu.:0.228268 3rd Qu.:29.00 3rd Qu.:71.50
## Max. :22826.3 Max. :1.490344 Max. :60.00 Max. :90.00
##
## Troph ReceiverDensity MonitArea_km2 MCP_km2
## Min. :2.860 Min. : 0.030 Min. : 0.690 Min. : 0.1447
## 1st Qu.:3.470 1st Qu.: 0.130 1st Qu.: 1.200 1st Qu.: 2.0148
## Median :4.090 Median : 0.330 Median : 3.700 Median : 12.2923
## Mean :3.926 Mean : 9.651 Mean : 6.226 Mean : 246.8819
## 3rd Qu.:4.270 3rd Qu.: 12.410 3rd Qu.: 8.380 3rd Qu.: 355.9882
## Max. :4.660 Max. :131.310 Max. :15.620 Max. :2990.2885
##
## NReceivers MaxDistReceivers MaxLength MaxBodyMass
## Min. : 4.00 Min. : 0.53 Min. : 38.0 Min. : 585
## 1st Qu.:15.00 1st Qu.: 3.21 1st Qu.: 55.0 1st Qu.: 2200
## Median :26.00 Median : 9.25 Median :122.0 Median :14300
## Mean :39.93 Mean :33.35 Mean :115.5 Mean :32029
## 3rd Qu.:45.00 3rd Qu.:74.03 3rd Qu.:177.6 3rd Qu.:60000
## Max. :98.00 Max. :87.39 Max. :200.0 Max. :96000
##
## a b SpawnSeason Tp
## Min. :0.00391 Min. :2.801 Length:23372 Min. : 2.0
## 1st Qu.:0.00900 1st Qu.:2.913 Class :character 1st Qu.: 293.0
## Median :0.01010 Median :3.060 Mode :character Median : 417.0
## Mean :0.01429 Mean :3.029 Mean : 753.4
## 3rd Qu.:0.01802 3rd Qu.:3.120 3rd Qu.:1467.0
## Max. :0.09550 Max. :3.280 Max. :2413.0
##
## RI ROM_mh Week Year Spawn
## Min. :0.000 Min. : 0.00 37 : 529 2008 :3847 A : 1025
## 1st Qu.:0.630 1st Qu.: 7.60 38 : 522 2011 :3527 SS:21108
## Median :0.810 Median : 19.40 35 : 508 2021 :2211 W : 1239
## Mean :0.752 Mean : 30.47 36 : 507 2009 :2020
## 3rd Qu.:0.940 3rd Qu.: 37.80 39 : 507 2007 :1851
## Max. :1.000 Max. :508.80 29 : 505 2010 :1487
## (Other):20294 (Other):8429boxplot(rem_out1_kud95$KUD95, col = "deepskyblue", ylab="KUD95", main="Boxplot of KUD95 after the first outliers removal")
boxplot.stats(week_kuds$KUD50) #superior limit where outliers start is 0.351## $stats
## [1] 0.162 0.164 0.173 0.239 0.351
##
## $n
## [1] 25612
##
## $conf
## [1] 0.1722595 0.1737405
##
## $out
## [1] 0.575 0.361 0.469 0.473 0.490 0.510 0.360 0.528 0.463 0.469 0.521 0.444
## [13] 0.467 0.477 0.494 0.393 0.463 0.480 0.469 0.490 0.456 0.417 0.423 0.422
## [25] 0.470 0.388 0.478 0.426 0.441 0.466 0.458 0.614 0.454 0.464 0.433 0.503
## [37] 0.467 0.428 0.385 0.395 0.395 0.474 0.405 0.436 0.458 0.406 0.374 0.364
## [49] 0.375 0.454 0.408 0.556 0.497 0.365 0.473 0.380 0.468 0.358 0.426 0.389
## [61] 0.376 0.367 0.401 0.483 0.455 0.365 0.467 0.574 0.591 0.588 0.374 0.574
## [73] 0.435 0.545 0.737 0.414 0.500 0.408 0.400 0.435 0.383 0.371 0.365 0.381
## [85] 0.370 0.352 0.386 0.432 0.401 0.365 0.381 0.390 0.365 0.377 0.393 0.464
## [97] 0.370 0.429 0.457 0.407 0.391 0.405 0.414 0.401 0.373 0.384 0.393 0.417
## [109] 0.393 0.488 0.431 0.422 0.522 0.488 0.367 0.479 0.394 0.360 0.371 0.364
## [121] 0.390 0.366 0.360 0.387 0.457 0.385 0.356 0.492 0.438 0.416 0.479 0.363
## [133] 0.569 0.447 0.428 0.426 0.445 0.361 0.418 0.532 0.466 0.396 0.424 0.377
## [145] 0.400 0.375 0.410 0.375 0.386 0.395 0.446 0.510 0.379 0.418 0.396 0.397
## [157] 0.442 0.463 0.360 0.489 0.475 0.465 0.359 0.484 0.420 0.375 0.387 0.531
## [169] 0.597 0.530 0.385 0.563 0.536 0.437 0.616 0.377 0.595 0.414 0.492 0.502
## [181] 0.519 0.538 0.579 0.359 0.382 0.369 0.440 0.402 0.474 0.574 1.394 1.494
## [193] 1.843 0.560 0.477 0.626 0.999 0.792 1.046 0.981 0.709 0.857 0.385 0.383
## [205] 0.405 0.498 0.361 0.582 0.555 0.500 0.402 0.574 0.568 0.449 0.438 0.583
## [217] 0.375 0.568 0.370 0.548 0.413 0.429 0.435 0.379 0.477 0.420 0.463 0.473
## [229] 0.426 0.424 0.437 0.359 0.427 0.408 0.372 0.974 0.859 0.566 0.369 0.374
## [241] 0.527 0.617 0.395 0.450 0.514 0.364 0.390 0.356 0.441 0.399 0.367 0.389
## [253] 0.384 0.447 0.490 0.463 0.368 0.529 0.433 0.395 0.371 0.428 0.453 0.449
## [265] 0.478 0.686 0.611 0.459 0.529 0.352 0.461 0.408 0.616 0.458 0.387 0.548
## [277] 0.682 0.402 0.624 0.420 0.413 0.713 0.408 0.608 0.701 0.682 0.464 0.465
## [289] 0.459 0.489 0.371 0.617 0.418 0.588 0.398 0.408 0.628 0.491 0.745 0.418
## [301] 0.407 0.627 0.432 0.683 0.379 0.367 0.749 0.636 0.783 0.381 0.407 0.406
## [313] 0.565 0.679 0.580 0.365 0.394 0.472 0.362 0.355 0.450 0.424 0.528 0.406
## [325] 0.427 0.400 0.448 0.388 0.466 0.360 0.368 0.466 0.827 0.400 0.400 0.380
## [337] 0.400 0.498 0.374 0.352 0.376 0.486 0.448 0.419 0.429 0.371 0.409 0.416
## [349] 0.412 0.404 0.489 0.632 0.428 0.356 0.528 0.748 0.456 1.049 0.620 0.444
## [361] 0.669 0.582 0.620 0.602 0.767 0.833 0.734 0.523 0.623 0.382 0.503 0.362
## [373] 0.403 0.441 0.445 0.456 0.492 0.479 0.485 0.642 0.575 0.489 1.126 0.394
## [385] 0.736 0.686 0.471 0.724 0.534 0.611 0.379 1.186 0.439 0.484 0.460 0.742
## [397] 0.817 0.920 1.098 0.698 0.442 0.467 0.361 0.835 0.357 0.779 0.389 0.721
## [409] 0.781 0.776 0.443 0.870 0.799 0.736 0.424 0.664 0.671 0.427 0.404 0.361
## [421] 0.441 0.507 0.740 0.694 0.666 0.627 0.528 0.432 0.362 0.425 0.403 0.407
## [433] 0.464 0.472 0.410 0.381 0.519 0.453 0.498 0.684 0.528 1.271 1.429 0.620
## [445] 0.389 0.366 0.959 0.718 0.427 0.685 0.550 1.510 0.457 0.665 0.374 0.431
## [457] 0.392 0.402 0.374 0.419 0.589 0.455 0.360 0.509 0.388 0.869 0.684 0.545
## [469] 0.400 0.388 0.469 0.741 0.840 0.668 1.089 1.092 0.994 0.543 0.524 0.903
## [481] 0.370 0.536 0.948 0.406 0.372 0.405 0.379 0.439 0.370 0.375 0.419 0.415
## [493] 0.429 0.374 0.369 0.448 0.354 0.360 0.408 0.430 0.458 0.406 0.406 0.369
## [505] 0.431 0.377 0.402 0.381 0.530 0.449 0.486 0.478 0.362 0.398 1.148 0.564
## [517] 0.458 0.503 0.540 0.614 0.686 0.474 0.491 0.397 0.443 0.804 1.049 0.423
## [529] 1.496 0.959 1.009 0.511 1.330 1.166 1.510 2.561 0.809 1.382 0.565 0.711
## [541] 0.645 3.104 0.564 2.383 0.487 1.296 0.465 0.506 1.168 1.557 1.180 0.613
## [553] 0.941 0.815 0.525 0.377 0.423 0.376 0.381 0.370 0.382 0.388 0.364 0.378
## [565] 0.379 0.455 0.526 0.419 0.361 0.447 0.378 0.423 0.384 0.452 0.416 0.353
## [577] 0.646 0.449 0.354 0.617 0.377 0.426 0.353 0.513 0.632 0.494 0.466 0.903
## [589] 0.522 0.602 0.702 0.442 0.437 0.667 0.693 0.666 0.564 0.470 0.717 0.691
## [601] 0.914 0.767 0.741 0.582 0.526 0.819 0.712 1.441 0.480 1.140 0.758 0.528
## [613] 0.366 0.519 0.690 0.458 0.882 0.663 0.715 0.806 0.615 0.509 0.635 0.549
## [625] 0.556 0.562 0.637 0.705 0.604 0.524 0.831 0.352 0.430 0.402 0.377 0.415
## [637] 0.557 0.407 0.710 0.465 0.625 0.398 0.715 0.423 0.505 0.615 0.911 0.739
## [649] 0.734 0.778 0.668 0.546 0.641 0.618 0.479 0.709 0.565 0.497 0.734 0.542
## [661] 1.003 0.486 0.838 0.615 0.519 0.389 0.379 0.376 0.404 0.406 0.374 0.374
## [673] 0.379 0.382 1.377 0.832 0.988 0.353 0.390 0.399 0.374 0.361 0.397 0.479
## [685] 0.371 0.394 0.365 0.369 0.372 0.353 0.394 0.451 0.378 0.503 0.388 0.512
## [697] 0.357 0.416 0.384 0.464 0.464 0.479 0.398 0.356 0.362 0.509 0.508 0.766
## [709] 0.516 0.645 0.502 0.556 0.442 0.514 0.381 0.500 0.603 0.383 0.398 0.457
## [721] 0.422 0.482 0.693 0.580 0.374 0.453 0.356 0.396 0.445 0.499 0.357 0.406
## [733] 0.362 0.384 0.420 0.465 0.414 0.354 0.437 0.374 0.353 0.385 0.399 0.415
## [745] 0.418 0.433 0.404 0.380 0.406 0.390 0.378 0.369 0.411 0.356 0.374 0.358
## [757] 0.368 0.373 0.355 0.396 0.402 0.375 0.372 0.378 0.398 0.382 0.370 0.355
## [769] 0.416 0.420 0.438 0.449 0.415 0.395 0.396 0.360 0.413 0.367 0.361 0.435
## [781] 0.438 0.363 0.439 0.356 0.368 0.659 0.509 0.368 0.618 0.374 0.366 0.575
## [793] 0.357 0.371 0.353 0.369 0.355 0.387 0.475 0.352 0.488 0.498 0.365 0.357
## [805] 0.361 0.564 0.411 0.530 0.453 0.360 0.484 0.370 0.689 0.368 0.419 0.379
## [817] 0.352 0.639 0.624 0.405 0.352 0.408 0.358 0.424 0.485 0.500 0.509 0.427
## [829] 0.463 0.462 0.429 0.371 0.412 0.358 0.368 0.485 0.886 0.556 0.570 0.393
## [841] 0.404 0.363 0.384 0.396 0.360 0.528 0.396 0.384 0.390 0.378 0.562 0.386
## [853] 0.370 0.408 0.355 0.396 0.370 0.376 0.395 0.358 0.474 0.361 0.379 0.372
## [865] 0.542 0.365 0.359 0.435 0.353 0.431 0.371 0.449 0.402 0.427 0.439 0.492
## [877] 0.459 0.362 0.356 0.357 0.367 0.352 0.377 0.370 0.375 0.423 0.367 0.386
## [889] 0.379 0.380 0.376 0.395 0.482 0.396 0.377 0.451 0.727 0.636 0.354 0.359
## [901] 0.368 0.387 0.769 0.364 0.396 0.360 0.373 0.436 0.609 0.440 0.432 0.703
## [913] 0.534 0.489 0.706 0.478 0.867 0.378 0.379 0.546 0.377 0.465 0.497 0.423
## [925] 0.495 0.514 0.449 0.399 0.437 0.370 0.381 0.352 0.371 0.463 0.375 0.597
## [937] 0.455 0.403 0.352 0.423 0.369 0.437 0.359 0.380 0.750 0.641 0.421 0.460
## [949] 0.572 0.446 0.461 0.448 0.392 0.625 0.511 0.752 0.422 0.689 0.367 0.464
## [961] 0.570 0.390 0.399 0.377 0.508 0.569 0.413 0.508 0.429 0.533 0.529 0.435
## [973] 0.595 0.562 0.562 0.357 0.398 0.373 0.367 0.353 0.408 0.413 0.475 0.688
## [985] 0.394 0.500 0.434 0.489 0.472 0.440 0.623 0.457 0.470 0.488 0.408 0.417
## [997] 0.499 0.458 0.520 0.449 0.469 0.453 0.357 0.412 0.366 0.425 0.354 0.430
## [1009] 0.370 0.377 0.352 0.427 0.649 0.465 0.486 0.477 0.472 0.380 0.372 0.410
## [1021] 0.439 0.406 0.468 0.440 0.358 0.352 0.363 0.484 0.498 0.356 0.592 0.384
## [1033] 0.366 0.358 0.380 0.358 0.352 0.501 0.409 0.439 0.458 0.486 0.477 0.357
## [1045] 0.573 0.492 0.706 0.607 0.492 0.536 0.426 0.360 0.407 0.405 0.773 0.373
## [1057] 0.504 0.389 0.372 0.521 0.663 0.382 0.593 1.032 0.737 0.466 0.430 0.740
## [1069] 1.139 0.969 0.844 0.886 0.556 0.854 0.610 0.604 0.765 0.594 0.569 0.884
## [1081] 0.932 0.935 1.141 0.929 0.841 0.409 0.743 0.858 0.383 0.482 0.502 1.032
## [1093] 0.546 0.511 0.470 0.378 0.367 0.415 0.394 0.427 0.530 0.499 0.435 0.467
## [1105] 0.386 0.424 0.389 0.432 0.379 0.432 0.575 0.471 0.411 0.544 0.453 0.423
## [1117] 0.383 0.644 0.376 0.409 1.002 1.644 0.609 1.024 0.366 0.830 1.231 0.465
## [1129] 0.462 0.385 0.407 0.713 0.524 0.486 0.430 1.243 0.934 0.620 0.467 0.529
## [1141] 0.379 0.707 0.728 1.108 0.730 1.815 0.362 0.588 0.525 1.322 0.373 0.520
## [1153] 0.444 0.578 1.563 1.095 0.840 0.611 0.804 0.701 0.805 0.977 0.580 0.452
## [1165] 0.357 0.386 0.377 1.584 1.109 0.531 0.722 1.350 0.842 0.586 1.062 0.393
## [1177] 0.382 0.564 0.867 0.861 0.562 0.745 1.095 0.901 0.456 0.465 0.696 0.641
## [1189] 0.882 0.919 0.854 0.569 0.533 0.914 1.505 0.782 0.731 0.890 0.904 0.644
## [1201] 0.702 0.430 0.666 0.429 0.440 0.580 0.470 1.003 0.865 0.395 0.891 0.754
## [1213] 0.497 0.604 0.490 0.762 0.499 0.390 0.381 0.372 0.739 0.424 0.409 0.499
## [1225] 0.531 0.577 0.737 0.714 0.683 0.364 0.370 0.352 0.355 0.390 0.617 0.857
## [1237] 0.485 0.496 0.430 0.413 0.430 0.522 0.489 0.406 0.354 0.730 0.539 0.759
## [1249] 0.708 0.701 0.387 0.491 1.129 0.445 0.619 0.505 0.703 0.356 0.409 0.387
## [1261] 0.360 0.465 0.675 0.460 0.447 0.420 0.407 0.500 0.526 0.927 0.947 0.374
## [1273] 0.377 0.484 0.385 0.410 0.492 0.413 0.413 0.395 0.409 0.389 0.494 0.462
## [1285] 0.414 0.412 0.376 0.457 0.368 0.374 0.570 0.375 0.412 0.549 0.550 0.803
## [1297] 0.473 0.434 0.397 0.373 0.839 0.420 0.355 0.650 0.458 0.380 0.522 0.373
## [1309] 0.608 0.563 0.532 0.462 0.796 0.473 0.829 0.467 0.520 0.554 0.587 0.671
## [1321] 0.422 0.413 0.362 0.557 0.354 0.352 0.606 0.443 0.366 0.819 0.439 0.838
## [1333] 0.547 0.490 0.965 0.690 0.788 0.603 0.533 0.607 0.732 0.709 0.773 0.405
## [1345] 0.754 0.432 0.524 0.630 0.449 0.518 0.366 0.425 0.422 0.535 0.528 0.674
## [1357] 0.714 0.403 0.482 0.406 0.357 0.361 0.405 0.513 0.375 0.457 0.417 0.826
## [1369] 0.425 0.546 0.740 0.615 0.524 0.521 0.627 0.671 0.724 0.358 0.477 0.365
## [1381] 0.434 0.608 0.479 0.508 0.627 0.436 0.560 0.524 0.595 0.542 0.615 0.624
## [1393] 0.378 0.400 0.497 0.413 0.481 0.420 0.429 0.407 0.435 0.434 0.436 0.455
## [1405] 0.424 0.411 0.407 0.444 0.435 0.402 0.424 0.384 0.417 0.403 0.362 0.397
## [1417] 0.376 0.387 0.470 0.411 0.489 0.380 0.360 0.362 0.354 0.387 0.392 0.501
## [1429] 0.354 0.589 0.366 0.556 0.522 0.425 0.462 0.813 0.545 0.616 0.794 0.459
## [1441] 0.540 0.665 0.466 0.590 0.574 0.751 0.484 0.803 0.384 0.457 0.559 0.469
## [1453] 0.549 0.668 0.688 0.367 0.802 0.484 0.598 1.067 0.412 0.553 0.361 0.358
## [1465] 0.681 0.420 0.822 0.579 0.424 0.523 0.532 1.013 0.768 0.848 0.835 0.852
## [1477] 0.909 0.797 0.415 0.682 0.515 0.372 0.461 0.386 0.537 0.352 0.461 0.439
## [1489] 0.557 0.522 0.550 0.588 0.675 0.680 0.728 0.476 0.418 0.498 0.400 0.394
## [1501] 0.409 0.437 0.618 0.445 1.277 1.229 0.597 1.118 2.046 0.811 0.754 0.401
## [1513] 0.378 0.364 0.438 0.353 0.491 0.414 0.481 0.399 0.546 0.479 0.584 0.573
## [1525] 0.601 0.721 0.826 0.465 0.393 0.409 0.535 0.391 0.563 0.530 0.417 0.542
## [1537] 0.559 0.475 0.737 0.471 0.651 0.845 0.463 0.593 0.623 0.486 0.596 0.599
## [1549] 0.884 0.510 0.624 0.501 0.661 0.577 0.561 0.388 0.676 0.929 0.589 0.576
## [1561] 0.586 0.842 1.132 0.374 0.459 0.889 0.533 0.434 0.553 1.126 1.143 0.712
## [1573] 0.931 0.776 0.548 0.384 0.535 0.487 0.841 0.416 0.362 0.496 0.455 0.355
## [1585] 0.489 0.517 0.485 0.525 0.728 0.634 0.789 0.875 0.625 0.529 0.631 0.511
## [1597] 0.362 0.580 0.889 0.696 0.823 0.681 0.635 0.451 0.479 0.449 0.391 0.610
## [1609] 0.375 0.462 0.394 1.086 0.431 0.607 0.358 0.598 2.022 1.748 0.431 0.702
## [1621] 0.732 0.983 0.395 0.499 1.275 0.395 0.382 0.356 0.484 0.379 0.383 0.490
## [1633] 0.569 0.461 0.472 0.443 0.400 0.356 0.387 0.378 0.463 0.388 0.377 0.427
## [1645] 0.394 0.461 0.637 0.415 0.499 0.412 0.369 0.392 0.489 0.487 0.481 0.427
## [1657] 0.357 0.511 0.471 0.369 0.475 0.398 0.381 0.480 0.439 0.481 0.402 0.385
## [1669] 0.430 0.397 0.360 0.500 0.456 0.366 0.381 0.384 0.399 0.433 0.436 0.352
## [1681] 0.385 0.406 0.482 0.398 0.360 0.504 0.412 0.575 0.388 0.524 0.423 0.385
## [1693] 0.357 0.362 0.369 1.109 0.654 0.564 0.388 0.478 0.676 0.641 0.746 0.862
## [1705] 2.086 1.283 1.586 1.243 0.911 1.584 1.096 0.911 1.493 0.520 0.537 0.576
## [1717] 0.453 0.830 0.557 0.361 0.489 0.424 0.362 0.523 0.545 0.440 0.452 0.585
## [1729] 0.461 0.545 0.598 0.560 0.428 0.426 0.629 0.397 0.430 0.489 0.578 0.405
## [1741] 0.518 0.404 0.388 0.492 0.565 0.439 0.462 0.467 0.368 0.579 0.745 0.721
## [1753] 0.644 0.460 0.365 0.456 2.034 0.381 0.424 0.435 0.413 0.682 3.278 0.355
## [1765] 0.373 0.367 0.365 0.820 0.477 0.638 0.375 0.378 0.454 0.365 0.414 0.361
## [1777] 0.398 0.425 0.597 0.373 0.352 0.520 0.368 0.366 0.362 0.402 0.586 0.499
## [1789] 0.359 0.486 0.403 0.502 0.367 0.394 0.391 0.430 0.382 0.437 0.442 0.358
## [1801] 0.404 0.359 0.393 0.358 0.385 0.428 0.431 0.505 0.583 0.726 0.620 0.733
## [1813] 0.723 0.680 0.425 0.877 0.473 0.359 0.851 0.357 0.408 0.396 0.498 0.444
## [1825] 0.465 0.388 0.505 0.442 0.730 0.816 0.490 0.559 0.584 0.390 0.426 0.494
## [1837] 0.498 0.357 0.360 0.413 0.605 0.718 0.512 0.380 0.369 0.580 0.367 0.353
## [1849] 0.380 0.455 0.415 0.761 0.357 0.402 0.375 1.305 0.352 0.466 0.644 0.735
## [1861] 0.840 0.631 0.521 0.376 0.425 0.450 0.457 0.422 0.358 0.359 0.364 0.357
## [1873] 0.398 0.401 0.366 0.490 0.510 0.603 0.457 0.476 0.359 0.376 0.452 0.398
## [1885] 0.353 0.382 0.540 1.085 0.650 0.399 0.475 0.378 0.408 0.368 0.352 0.493
## [1897] 0.470 0.360 0.404 0.598 0.470 0.606 0.542 0.695 0.461 0.569 0.471 0.755
## [1909] 0.611 0.460 0.386 0.372 0.450 0.419 0.378 0.465 0.520 0.391 0.523 0.655
## [1921] 0.501 0.422 0.435 0.396 0.352 0.401 0.435 0.547 0.466 0.645 0.438 0.454
## [1933] 0.427 0.383 0.410 0.406 0.465 0.389 0.502 0.394 0.430 0.387 0.435 0.385
## [1945] 0.360 0.398 0.484 0.569 0.397 0.412 0.381 0.566 0.401 0.382 0.568 0.560
## [1957] 0.519 0.530 0.616 0.526 0.560 0.404 0.570 0.640 0.436 0.558 0.630 0.530
## [1969] 0.598 0.556 0.554 0.402 0.450 0.465 0.513 0.433 0.407 0.491 0.473 0.356
## [1981] 0.399 0.450 0.353 0.377 0.379 0.454 0.390 0.406 0.472 0.502 0.362 0.437
## [1993] 0.360 0.357 0.357 0.368 0.358 0.354 0.354 0.358 0.519 0.411 0.612 0.410
## [2005] 0.519 0.411 0.463 0.512 0.586 0.413 0.562 0.559 0.516 0.514 0.419 0.431
## [2017] 0.437 0.446 0.432 0.355 0.593 0.409 0.394 0.592 0.408 0.472 0.382 0.578
## [2029] 0.360 0.436 0.530 0.428 0.372 0.421 0.361 0.376 0.491 0.463 0.369 0.447
## [2041] 0.479 0.409 0.392 0.357 0.412 0.355 0.526 0.436 0.449 0.384 0.363 0.352
## [2053] 0.403 0.403 0.407 0.355 0.369 0.490 0.392 0.415 0.453 0.436 0.490 0.402
## [2065] 0.392 0.403 0.385 0.382 0.681 0.672 0.698 0.599 0.419 0.449 0.404 0.545
## [2077] 0.364 0.562 0.615 0.487 0.568 0.662 0.703 0.572 0.516 0.652 0.873 0.475
## [2089] 0.399 0.690 1.443 0.564 0.509 0.419 0.481 0.612 0.970 0.435 0.599 0.671
## [2101] 0.916 0.675 0.390 0.453 0.792 1.186 0.935 0.726 0.851 0.435 0.629 0.718
## [2113] 1.008 0.454 0.597 0.432 0.373 0.444 0.544 0.653 0.377 0.702 0.398 0.469
## [2125] 0.391 0.442 0.365 0.362 0.601 0.522 0.605 0.541 0.534 0.636 0.449 0.439
## [2137] 0.486 0.431 0.454 0.412 0.396 0.581 0.732 0.614 0.475 0.406 0.591 0.571
## [2149] 0.381 0.490 0.411 0.586 0.382 0.364 0.664 1.527 1.016 0.919 0.768 0.758
## [2161] 0.820 1.663 0.569 0.666 0.421 1.079 0.777 1.020 1.115 0.361 0.565 0.433
## [2173] 0.363 0.381 0.443 0.373 0.560 0.416 0.424 0.429 0.593 0.374 0.606 0.488
## [2185] 0.371 0.577 0.411 0.826 0.951 0.759 0.450 0.668 0.461 0.620 0.584 1.047
## [2197] 1.089 0.895 0.372 0.797 0.392 1.930 0.472 0.502 0.496 0.473 0.443 0.956
## [2209] 1.067 0.772 0.890 0.872 1.188 0.431 0.395 0.538 0.413 0.429 0.371 0.532
## [2221] 0.450 0.511 0.409 0.528 0.371 0.366 0.424 0.486 0.437 0.386 0.500 0.528
## [2233] 0.588 0.421 1.350 0.635 1.623 0.815 0.565 0.395 0.929 0.742 0.458 0.456
## [2245] 0.382 0.358 1.794 0.455 0.544 0.365 1.586 0.453 0.728 1.075 0.937 0.786
## [2257] 0.506 0.659 0.763 0.393 0.372 0.388 0.448 0.482 0.507 0.543 0.535 0.441
## [2269] 0.592 0.671 0.436 0.525 0.401 0.404 0.376 0.374 0.368 0.449 0.432 0.394
## [2281] 0.405 0.360 0.519 0.452 0.516 0.407 0.551 0.373 0.406 0.417 0.409 0.447
## [2293] 0.612 0.355 0.794 0.489 0.916 0.633 0.551 0.590 0.609 0.658 0.635 0.701
## [2305] 0.470 0.379 0.409 0.680 0.956 0.362 0.616 0.396 0.358 0.372 0.386 0.463
## [2317] 0.463 0.497 0.400 0.383 0.354 0.395 0.412 0.493 0.400 0.631 0.358 0.380
## [2329] 1.395 0.820 0.537 0.818 0.924 0.920 0.481 0.584 0.359 0.462 0.438 0.995
## [2341] 1.124 0.430 0.656 0.472 0.395 0.386 0.852 0.385 0.369 0.397 0.413 0.413
## [2353] 0.376 0.480 0.495 0.457 0.507 0.514 0.486 0.425 0.457 0.548 0.587 0.508
## [2365] 0.438 0.591 0.479 0.460 0.408 0.364 0.365 0.517 0.433 0.524 0.411 0.359
## [2377] 0.407 0.360 0.371 0.459 0.447 0.466 0.547 0.424 0.441 0.554 0.361 0.361
## [2389] 0.440 0.389 0.363 0.432 0.369 0.424 0.355 0.379 0.393 0.363 0.401 0.383
## [2401] 0.406 0.382 0.373 0.362 0.475 0.370 0.365 0.361 0.403 0.405 0.413 0.386
## [2413] 0.369 0.352 0.374 0.366 0.382 0.441 0.400 0.355 0.526 0.354 0.390 0.466
## [2425] 0.441 0.364 0.491rem_out1_kud50 <- subset(week_kuds, KUD50 < 0.351)
boxplot(rem_out1_kud50$KUD50, col = "green2", ylab="KUD50", main="Boxplot of KUD50 after the first outliers removal")
#Second removal
boxplot.stats(rem_out1_kud95$KUD95) #superior limit where outliers start is 1.494## $stats
## [1] 0.7600 0.7620 0.7935 1.0550 1.4940
##
## $n
## [1] 23372
##
## $conf
## [1] 0.7904719 0.7965281
##
## $out
## [1] 1.772 1.812 1.736 1.812 1.818 1.800 1.790 1.642 1.577 1.780 1.762 1.762
## [13] 1.803 1.782 1.819 1.496 1.629 1.738 1.714 1.779 1.697 1.587 1.733 1.755
## [25] 1.751 1.797 1.726 1.699 1.685 1.774 1.814 1.724 1.799 1.668 1.716 1.673
## [37] 1.788 1.553 1.659 1.621 1.606 1.702 1.512 1.652 1.552 1.653 1.734 1.679
## [49] 1.728 1.763 1.623 1.531 1.611 1.516 1.508 1.612 1.508 1.781 1.802 1.597
## [61] 1.793 1.560 1.751 1.593 1.651 1.648 1.538 1.529 1.502 1.803 1.646 1.820
## [73] 1.599 1.736 1.510 1.825 1.512 1.623 1.535 1.685 1.732 1.806 1.748 1.584
## [85] 1.672 1.554 1.784 1.504 1.516 1.815 1.667 1.564 1.578 1.578 1.570 1.509
## [97] 1.720 1.520 1.606 1.520 1.505 1.495 1.648 1.567 1.639 1.669 1.710 1.537
## [109] 1.658 1.686 1.537 1.576 1.566 1.650 1.516 1.527 1.541 1.525 1.629 1.797
## [121] 1.642 1.786 1.735 1.736 1.705 1.679 1.604 1.620 1.654 1.694 1.752 1.825
## [133] 1.719 1.522 1.710 1.630 1.816 1.535 1.721 1.519 1.703 1.764 1.500 1.501
## [145] 1.546 1.783 1.807 1.625 1.705 1.687 1.626 1.773 1.752 1.673 1.746 1.788
## [157] 1.767 1.625 1.672 1.548 1.501 1.507 1.495 1.631 1.620 1.587 1.661 1.777
## [169] 1.652 1.582 1.653 1.782 1.723 1.561 1.742 1.623 1.732 1.820 1.722 1.820
## [181] 1.761 1.655 1.696 1.567 1.713 1.585 1.573 1.575 1.509 1.498 1.760 1.794
## [193] 1.717 1.689 1.608 1.796 1.535 1.620 1.588 1.749 1.532 1.722 1.723 1.550
## [205] 1.674 1.667 1.710 1.516 1.780 1.617 1.793 1.687 1.499 1.819 1.806 1.700
## [217] 1.559 1.644 1.657 1.791 1.496 1.601 1.642 1.525 1.681 1.669 1.765 1.781
## [229] 1.587 1.798 1.740 1.747 1.659 1.516 1.819 1.826 1.748 1.761 1.779 1.619
## [241] 1.705 1.756 1.517 1.703 1.567 1.601 1.629 1.522 1.561 1.787 1.717 1.820
## [253] 1.576 1.553 1.534 1.497 1.708 1.787 1.726 1.726 1.640 1.593 1.528 1.527
## [265] 1.516 1.516 1.517 1.517 1.517 1.516 1.526 1.524 1.714 1.593 1.736 1.513
## [277] 1.516 1.526 1.516 1.554 1.725 1.558 1.717 1.516 1.524 1.809 1.516 1.496
## [289] 1.521 1.632 1.517 1.745 1.670 1.699 1.554 1.496 1.534 1.499 1.723 1.822
## [301] 1.728 1.765 1.651 1.628 1.715 1.742 1.743 1.731 1.593 1.697 1.497 1.525
## [313] 1.694 1.636 1.517 1.704 1.729 1.716 1.517 1.725 1.694 1.668 1.742 1.501
## [325] 1.562 1.513 1.652 1.639 1.572 1.517 1.771 1.753 1.679 1.620 1.666 1.674
## [337] 1.723 1.672 1.742 1.753 1.691 1.645 1.695 1.670 1.514 1.535 1.517 1.532
## [349] 1.496 1.502 1.737 1.647 1.739 1.632 1.615 1.825 1.803 1.499 1.524 1.508
## [361] 1.523 1.528 1.519 1.495 1.523 1.526 1.813 1.809 1.691 1.510 1.527 1.524
## [373] 1.526 1.526 1.513 1.535 1.521 1.527 1.526 1.524 1.525 1.516 1.531 1.527
## [385] 1.803 1.590 1.740 1.548 1.819 1.644 1.584 1.529 1.729 1.751 1.527 1.619
## [397] 1.743 1.712 1.679 1.703 1.627 1.526 1.699 1.745 1.512 1.817 1.640 1.592
## [409] 1.698 1.824 1.676 1.701 1.661 1.578 1.574 1.519 1.548 1.662 1.560 1.520
## [421] 1.724 1.611 1.502 1.527 1.501 1.541 1.585 1.560 1.559 1.637 1.561 1.569
## [433] 1.543 1.613 1.625 1.663 1.562 1.523 1.562 1.547 1.693 1.825 1.724 1.726
## [445] 1.569 1.522 1.587 1.756 1.785 1.813 1.740 1.649 1.610 1.676 1.678 1.719
## [457] 1.629 1.598 1.743 1.677 1.525 1.686 1.741 1.737 1.805 1.796 1.739 1.504
## [469] 1.509 1.520 1.509 1.519 1.634 1.531 1.500 1.501 1.524 1.550 1.526 1.497
## [481] 1.776 1.685 1.683 1.564 1.515 1.518 1.807 1.688 1.566 1.741 1.657 1.535
## [493] 1.674 1.685 1.667 1.558 1.552 1.496 1.624 1.569 1.579 1.710 1.528 1.755
## [505] 1.596 1.646 1.583 1.686 1.592 1.670 1.557 1.594 1.560 1.527 1.545 1.512
## [517] 1.609 1.686 1.551 1.668 1.772 1.520 1.589 1.507 1.566 1.538 1.678 1.690
## [529] 1.529 1.638 1.543 1.525 1.533 1.519 1.563 1.546 1.562 1.570 1.522 1.748
## [541] 1.824 1.502 1.690 1.720 1.783 1.638 1.679 1.699 1.722 1.628 1.700 1.762
## [553] 1.734 1.812 1.631 1.631 1.598 1.568 1.727 1.552 1.684 1.596 1.775 1.707
## [565] 1.549 1.580 1.769 1.746 1.514 1.751 1.520 1.755 1.826 1.511 1.618 1.767
## [577] 1.759 1.703 1.585 1.637 1.512 1.706 1.579 1.524 1.739 1.736 1.584 1.523
## [589] 1.629 1.661 1.664 1.526 1.532 1.554 1.704 1.797 1.561 1.825 1.770 1.779
## [601] 1.502 1.660 1.635 1.805 1.637 1.738 1.750 1.723 1.581 1.604 1.604 1.504
## [613] 1.615 1.528 1.636 1.570 1.685 1.785 1.703 1.498 1.641 1.550 1.651 1.645
## [625] 1.534 1.774 1.581 1.583 1.608 1.755 1.572 1.519 1.695 1.598 1.565 1.741
## [637] 1.715 1.712 1.667 1.729 1.735 1.604 1.691 1.707 1.642 1.700 1.678 1.539
## [649] 1.800 1.565 1.732 1.615 1.619 1.503 1.800 1.705 1.754 1.709 1.692 1.778
## [661] 1.609 1.736 1.516 1.577 1.600 1.731 1.503 1.758 1.596 1.622 1.694 1.645
## [673] 1.764 1.517 1.558 1.651 1.783 1.780 1.768 1.779 1.662 1.673 1.808 1.760
## [685] 1.657 1.531 1.573 1.574 1.554 1.553 1.658 1.675 1.503 1.546 1.507 1.619
## [697] 1.557 1.791 1.741 1.663 1.628 1.555 1.612 1.739 1.623 1.703 1.712 1.543
## [709] 1.549 1.628 1.675 1.580 1.542 1.569 1.518 1.602 1.591 1.521 1.604 1.587
## [721] 1.510 1.633 1.502 1.512 1.504 1.686 1.682 1.521 1.726 1.612 1.706 1.812
## [733] 1.614 1.599 1.551 1.584 1.752 1.672 1.676 1.507 1.644 1.736 1.498 1.633
## [745] 1.496 1.524 1.524 1.507 1.551 1.810 1.601 1.515 1.567 1.625 1.600 1.684
## [757] 1.581 1.515 1.532 1.641 1.496 1.553 1.571 1.792 1.518 1.527 1.648 1.545
## [769] 1.783 1.646 1.808 1.656 1.788 1.525 1.503 1.767 1.730 1.504 1.705 1.506
## [781] 1.799 1.729 1.531 1.673 1.595 1.541 1.741 1.519 1.541 1.816 1.739 1.571
## [793] 1.594 1.756 1.496 1.564 1.549 1.688 1.511 1.663 1.778 1.721 1.523 1.679
## [805] 1.662 1.771 1.509 1.662 1.672 1.672 1.808 1.512 1.540 1.594 1.546 1.516
## [817] 1.514 1.820 1.496 1.517 1.522 1.809 1.525 1.826 1.520 1.507 1.821 1.815
## [829] 1.786 1.668 1.704 1.563 1.728 1.534 1.680 1.633 1.617 1.605 1.666 1.499
## [841] 1.594 1.793 1.590 1.523 1.608 1.497 1.640 1.539 1.519 1.528 1.517 1.530
## [853] 1.615 1.528 1.641 1.505 1.534 1.530 1.575 1.530 1.575 1.634 1.498 1.742
## [865] 1.520 1.721 1.496 1.496 1.522 1.496 1.618 1.623 1.564 1.655 1.587 1.525
## [877] 1.516 1.496 1.676 1.687 1.574 1.626 1.785 1.675 1.655 1.518 1.510 1.536
## [889] 1.808 1.552 1.554 1.537 1.556 1.563 1.566 1.592 1.610 1.536 1.600 1.575
## [901] 1.506 1.537 1.556 1.585 1.554 1.574 1.600 1.556 1.568 1.809 1.584 1.673
## [913] 1.747 1.761 1.754 1.794 1.721 1.713 1.635 1.628 1.606 1.714 1.623 1.638
## [925] 1.514 1.811 1.713 1.646 1.539 1.762 1.744 1.754 1.668 1.551 1.701 1.809
## [937] 1.639 1.820 1.738 1.540 1.628 1.777 1.792 1.533 1.588 1.587 1.523 1.528
## [949] 1.628 1.572 1.774 1.702 1.660 1.694 1.693 1.635 1.605 1.684 1.823 1.601
## [961] 1.809 1.593 1.706 1.542 1.738 1.720 1.798 1.648 1.723 1.631 1.519 1.666
## [973] 1.704 1.587 1.763 1.564 1.658 1.710 1.633 1.804 1.740 1.574 1.634 1.540
## [985] 1.501 1.716 1.639 1.520 1.525 1.517 1.516 1.512 1.528 1.527 1.527 1.524
## [997] 1.522 1.524 1.510 1.636 1.507 1.516 1.505 1.517 1.524 1.513 1.523 1.500
## [1009] 1.523 1.517 1.806 1.497 1.527 1.517 1.517 1.509 1.518 1.751 1.517 1.606
## [1021] 1.663 1.528 1.754 1.527 1.802 1.528 1.518 1.774 1.526 1.756 1.501 1.734
## [1033] 1.526 1.509 1.527 1.552 1.807 1.518 1.500 1.506 1.499 1.781 1.524 1.506
## [1045] 1.527 1.522 1.510 1.522 1.810 1.526 1.524 1.602 1.523 1.519 1.518 1.505
## [1057] 1.532 1.527 1.516 1.522 1.523 1.510 1.528 1.511 1.496 1.726 1.521 1.517
## [1069] 1.496 1.507 1.517 1.763 1.680 1.637 1.527 1.667 1.507 1.795 1.526 1.744
## [1081] 1.522 1.497 1.521 1.509 1.506 1.526 1.507 1.810 1.518 1.497 1.518 1.527
## [1093] 1.520 1.497 1.528 1.508 1.668 1.527 1.510 1.517 1.517 1.764 1.525 1.523
## [1105] 1.527 1.517 1.526 1.593 1.522 1.527 1.802 1.527 1.524 1.646 1.524 1.812
## [1117] 1.528 1.528 1.518 1.528 1.503 1.521 1.767 1.519 1.508 1.520 1.522 1.519
## [1129] 1.505 1.769 1.523 1.516 1.517 1.528 1.507 1.522 1.517 1.517 1.520 1.527
## [1141] 1.709 1.655 1.506 1.758 1.683 1.523 1.520 1.524 1.517 1.528 1.525 1.509
## [1153] 1.526 1.500 1.505 1.504 1.519 1.497 1.510 1.505 1.509 1.528 1.523 1.516
## [1165] 1.524 1.626 1.527 1.763 1.525 1.503 1.553 1.516 1.523 1.522 1.634 1.512
## [1177] 1.506 1.527 1.508 1.510 1.523 1.526 1.516 1.497 1.524 1.496 1.814 1.517
## [1189] 1.513 1.527 1.502 1.497 1.504 1.511 1.629 1.526 1.503 1.524 1.521 1.523
## [1201] 1.790 1.589 1.600 1.514 1.560 1.740 1.594 1.713 1.640 1.765 1.507 1.689
## [1213] 1.519 1.637 1.819 1.678 1.626 1.747 1.641 1.693 1.496 1.798 1.639 1.545
## [1225] 1.652 1.609 1.556 1.769 1.731 1.570 1.802 1.775 1.499 1.695 1.497 1.708
## [1237] 1.584 1.815 1.629 1.667 1.585 1.816 1.570 1.525 1.626 1.655 1.620 1.811
## [1249] 1.625 1.804 1.567 1.767 1.631 1.812 1.520 1.548 1.594 1.729 1.560 1.630
## [1261] 1.725 1.722 1.785 1.675 1.632 1.700 1.527 1.585 1.610 1.506 1.701 1.524
## [1273] 1.704 1.817 1.688 1.554 1.625 1.677 1.819 1.663 1.644 1.790 1.604 1.638
## [1285] 1.543 1.544 1.625 1.739 1.572 1.662 1.701 1.528 1.615 1.543 1.584 1.551
## [1297] 1.684 1.692 1.736 1.662 1.645 1.695 1.676 1.681 1.637 1.789 1.564 1.743
## [1309] 1.635 1.800 1.553 1.822 1.583 1.768 1.560 1.563 1.551 1.513 1.532 1.584
## [1321] 1.751 1.757 1.726 1.823 1.651 1.603 1.608 1.625 1.771 1.515 1.768 1.548
## [1333] 1.617 1.496 1.640 1.784 1.652 1.721 1.683 1.782 1.526 1.714 1.628 1.795
## [1345] 1.662 1.590 1.586 1.580 1.597 1.495 1.572 1.510 1.582 1.712 1.656 1.657
## [1357] 1.515 1.556 1.714 1.505 1.527 1.526 1.527 1.518 1.785 1.515 1.526 1.604
## [1369] 1.515 1.508 1.525 1.514 1.522 1.790 1.753 1.593 1.527 1.523 1.504 1.500
## [1381] 1.520 1.708 1.503 1.528 1.603 1.518 1.527 1.506 1.524 1.686 1.632 1.526
## [1393] 1.746 1.527 1.768 1.553 1.521 1.505 1.527 1.515 1.511 1.514 1.517 1.513
## [1405] 1.517 1.524 1.520 1.514 1.527 1.501 1.526 1.657 1.507 1.525 1.524 1.527
## [1417] 1.517 1.511 1.524 1.740 1.507 1.527 1.527 1.528 1.507 1.523 1.619 1.511
## [1429] 1.503 1.503 1.619 1.504 1.655 1.509 1.526 1.518 1.511 1.805 1.523 1.796
## [1441] 1.527 1.499 1.527 1.502 1.517 1.496 1.507 1.527 1.517 1.522 1.598 1.572
## [1453] 1.710 1.660 1.581 1.712 1.674 1.622 1.702 1.607 1.791 1.779 1.826 1.781
## [1465] 1.675 1.803 1.579 1.742 1.600 1.763 1.686 1.676 1.662 1.551 1.681 1.698
## [1477] 1.654 1.674 1.579 1.766 1.583 1.634 1.715 1.687 1.610 1.697 1.640 1.684
## [1489] 1.587 1.788 1.566 1.571 1.613 1.715 1.607 1.521 1.719 1.498 1.707 1.541
## [1501] 1.601 1.701 1.572 1.648 1.684 1.658 1.518 1.780 1.826 1.685 1.751 1.654
## [1513] 1.512 1.752 1.773 1.786 1.599 1.624 1.723 1.520 1.684 1.723 1.554 1.750
## [1525] 1.504 1.524 1.726 1.779 1.731 1.655 1.615 1.801 1.654 1.798 1.646 1.501
## [1537] 1.555 1.652 1.716 1.694 1.772 1.758 1.767 1.740 1.548 1.649 1.594 1.738
## [1549] 1.548 1.601 1.807 1.784 1.503 1.776 1.793 1.531 1.539 1.527 1.523 1.519
## [1561] 1.512 1.623 1.818 1.675 1.711 1.637 1.556 1.498 1.503 1.783 1.700 1.652
## [1573] 1.569 1.600 1.640 1.518 1.706 1.566 1.688 1.729rem_out2_kud95 <- subset(week_kuds, KUD95 < 1.494)
boxplot(rem_out2_kud95$KUD95, col = "deepskyblue", ylab="KUD95", main="Boxplot of KUD95 after the second outliers removal")
boxplot.stats(rem_out1_kud50$KUD50) #superior limit where outliers start is 0.281## $stats
## [1] 0.162 0.164 0.169 0.211 0.281
##
## $n
## [1] 23164
##
## $conf
## [1] 0.1685121 0.1694879
##
## $out
## [1] 0.340 0.295 0.308 0.345 0.309 0.334 0.326 0.347 0.349 0.294 0.290 0.300
## [13] 0.287 0.289 0.288 0.293 0.311 0.317 0.340 0.284 0.340 0.337 0.283 0.285
## [25] 0.302 0.339 0.286 0.292 0.325 0.330 0.290 0.348 0.290 0.291 0.337 0.343
## [37] 0.301 0.344 0.321 0.329 0.314 0.314 0.341 0.349 0.350 0.307 0.311 0.303
## [49] 0.299 0.291 0.314 0.350 0.292 0.287 0.319 0.287 0.282 0.316 0.293 0.293
## [61] 0.298 0.302 0.323 0.315 0.282 0.332 0.312 0.299 0.334 0.292 0.321 0.338
## [73] 0.332 0.307 0.316 0.300 0.309 0.289 0.319 0.295 0.307 0.326 0.288 0.289
## [85] 0.285 0.305 0.283 0.282 0.283 0.286 0.316 0.293 0.341 0.282 0.289 0.308
## [97] 0.307 0.292 0.295 0.295 0.337 0.330 0.344 0.298 0.334 0.287 0.333 0.289
## [109] 0.330 0.350 0.338 0.326 0.285 0.302 0.349 0.286 0.299 0.297 0.296 0.293
## [121] 0.282 0.318 0.324 0.318 0.286 0.330 0.321 0.307 0.337 0.291 0.290 0.345
## [133] 0.283 0.284 0.297 0.285 0.300 0.308 0.300 0.338 0.330 0.305 0.324 0.333
## [145] 0.311 0.304 0.297 0.331 0.292 0.337 0.314 0.348 0.292 0.307 0.320 0.283
## [157] 0.331 0.316 0.337 0.312 0.328 0.338 0.323 0.329 0.328 0.328 0.298 0.303
## [169] 0.325 0.311 0.339 0.338 0.344 0.347 0.303 0.298 0.293 0.344 0.334 0.294
## [181] 0.346 0.336 0.346 0.285 0.283 0.319 0.332 0.326 0.306 0.314 0.291 0.334
## [193] 0.309 0.349 0.302 0.283 0.311 0.294 0.287 0.346 0.336 0.287 0.297 0.350
## [205] 0.290 0.305 0.345 0.327 0.310 0.285 0.294 0.334 0.329 0.292 0.328 0.301
## [217] 0.329 0.325 0.301 0.283 0.284 0.344 0.317 0.320 0.316 0.326 0.305 0.294
## [229] 0.330 0.332 0.333 0.322 0.323 0.323 0.322 0.323 0.322 0.332 0.330 0.307
## [241] 0.319 0.322 0.331 0.322 0.302 0.331 0.343 0.322 0.300 0.329 0.327 0.333
## [253] 0.322 0.295 0.285 0.303 0.301 0.305 0.315 0.329 0.339 0.294 0.318 0.322
## [265] 0.319 0.349 0.331 0.304 0.332 0.337 0.283 0.322 0.346 0.322 0.286 0.343
## [277] 0.319 0.293 0.322 0.295 0.297 0.306 0.329 0.316 0.328 0.299 0.339 0.345
## [289] 0.297 0.322 0.330 0.324 0.329 0.300 0.307 0.319 0.312 0.309 0.318 0.327
## [301] 0.301 0.329 0.313 0.330 0.331 0.326 0.299 0.331 0.330 0.293 0.284 0.343
## [313] 0.291 0.348 0.333 0.328 0.332 0.333 0.320 0.288 0.330 0.326 0.333 0.330
## [325] 0.330 0.332 0.323 0.329 0.331 0.319 0.331 0.349 0.294 0.331 0.288 0.347
## [337] 0.297 0.308 0.306 0.304 0.337 0.343 0.337 0.295 0.322 0.343 0.347 0.319
## [349] 0.285 0.343 0.283 0.308 0.325 0.310 0.345 0.314 0.287 0.283 0.321 0.310
## [361] 0.316 0.338 0.345 0.287 0.330 0.349 0.311 0.296 0.298 0.283 0.293 0.285
## [373] 0.331 0.342 0.339 0.330 0.344 0.303 0.338 0.340 0.340 0.284 0.350 0.321
## [385] 0.312 0.327 0.297 0.327 0.327 0.292 0.295 0.315 0.337 0.338 0.337 0.350
## [397] 0.304 0.313 0.288 0.348 0.326 0.292 0.332 0.320 0.311 0.333 0.345 0.345
## [409] 0.342 0.304 0.310 0.328 0.337 0.301 0.312 0.334 0.316 0.320 0.333 0.322
## [421] 0.296 0.329 0.294 0.291 0.314 0.328 0.325 0.341 0.308 0.282 0.325 0.328
## [433] 0.290 0.285 0.310 0.340 0.348 0.284 0.283 0.302 0.302 0.305 0.335 0.325
## [445] 0.311 0.287 0.322 0.298 0.336 0.316 0.325 0.325 0.344 0.334 0.306 0.290
## [457] 0.296 0.316 0.304 0.295 0.332 0.325 0.302 0.348 0.325 0.329 0.323 0.321
## [469] 0.289 0.323 0.336 0.332 0.321 0.300 0.335 0.325 0.305 0.311 0.313 0.288
## [481] 0.287 0.299 0.330 0.347 0.334 0.323 0.303 0.316 0.287 0.304 0.313 0.293
## [493] 0.296 0.294 0.316 0.298 0.305 0.282 0.334 0.307 0.332 0.285 0.332 0.288
## [505] 0.327 0.325 0.290 0.336 0.340 0.335 0.343 0.287 0.339 0.317 0.336 0.334
## [517] 0.290 0.283 0.291 0.298 0.290 0.349 0.338 0.300 0.337 0.332 0.318 0.308
## [529] 0.290 0.282 0.284 0.283 0.302 0.283 0.290 0.298 0.282 0.319 0.302 0.290
## [541] 0.342 0.334 0.341 0.344 0.302 0.320 0.315 0.296 0.290 0.291 0.314 0.319
## [553] 0.324 0.294 0.320 0.343 0.319 0.341 0.317 0.314 0.342 0.337 0.344 0.318
## [565] 0.294 0.322 0.314 0.335 0.308 0.317 0.294 0.300 0.283 0.301 0.291 0.325
## [577] 0.319 0.330 0.333 0.311 0.338 0.339 0.314 0.333 0.282 0.299 0.304 0.286
## [589] 0.282 0.285 0.282 0.317 0.289 0.342 0.326 0.308 0.330 0.336 0.336 0.318
## [601] 0.282 0.311 0.333 0.327 0.303 0.344 0.330 0.330 0.299 0.325 0.310 0.340
## [613] 0.307 0.341 0.313 0.298 0.344 0.328 0.338 0.297 0.289 0.297 0.285 0.300
## [625] 0.289 0.350 0.328 0.298 0.350 0.298 0.330 0.329 0.331 0.318 0.316 0.295
## [637] 0.311 0.327 0.302 0.295 0.289 0.305 0.312 0.284 0.289 0.300 0.322 0.318
## [649] 0.303 0.292 0.303 0.295 0.311 0.347 0.283 0.310 0.311 0.325 0.314 0.307
## [661] 0.311 0.288 0.343 0.330 0.294 0.310 0.320 0.307 0.309 0.336 0.282 0.308
## [673] 0.303 0.300 0.298 0.296 0.299 0.319 0.308 0.309 0.323 0.292 0.347 0.287
## [685] 0.321 0.295 0.344 0.328 0.292 0.286 0.313 0.340 0.343 0.284 0.321 0.292
## [697] 0.331 0.349 0.294 0.284 0.283 0.298 0.282 0.325 0.331 0.345 0.283 0.282
## [709] 0.304 0.291 0.314 0.337 0.304 0.302 0.324 0.287 0.282 0.285 0.290 0.284
## [721] 0.330 0.337 0.298 0.323 0.306 0.303 0.320 0.294 0.304 0.306 0.302 0.320
## [733] 0.286 0.294 0.314 0.289 0.297 0.310 0.309 0.300 0.312 0.305 0.316 0.324
## [745] 0.292 0.289 0.311 0.283 0.296 0.302 0.304 0.288 0.337 0.340 0.302 0.328
## [757] 0.333 0.292 0.314 0.305 0.309 0.326 0.321 0.304 0.333 0.297 0.292 0.319
## [769] 0.314 0.287 0.289 0.288 0.329 0.317 0.321 0.299 0.307 0.290 0.330 0.284
## [781] 0.296 0.296 0.305 0.323 0.286 0.283 0.285 0.286 0.282 0.286 0.307 0.293
## [793] 0.303 0.320 0.322 0.290 0.285 0.314 0.344 0.293 0.326 0.337 0.337 0.286
## [805] 0.328 0.285 0.319 0.293 0.334 0.289 0.302 0.290 0.288 0.302 0.286 0.297
## [817] 0.282 0.296 0.316 0.299 0.343 0.327 0.317 0.296 0.296 0.301 0.331 0.287
## [829] 0.322 0.303 0.283 0.284 0.301 0.317 0.299 0.285 0.313 0.310 0.327 0.332
## [841] 0.321 0.322 0.292 0.333 0.320 0.338 0.325 0.345 0.327 0.294 0.307 0.314
## [853] 0.323 0.343 0.310 0.306 0.324 0.284 0.323 0.326 0.347 0.339 0.325 0.317
## [865] 0.301 0.336 0.339 0.313 0.297 0.322 0.340 0.316 0.321 0.343 0.336 0.342
## [877] 0.297 0.285 0.288 0.305 0.326 0.324 0.301 0.305 0.285 0.300 0.315 0.296
## [889] 0.288 0.298 0.298 0.319 0.318 0.324 0.333 0.303 0.289 0.308 0.289 0.290
## [901] 0.331 0.341 0.319 0.295 0.296 0.312 0.338 0.332 0.309 0.296 0.286 0.292
## [913] 0.296 0.309 0.323 0.334 0.282 0.283 0.335 0.331 0.292 0.319 0.283 0.340
## [925] 0.287 0.283 0.306 0.328 0.303 0.327 0.331 0.291 0.335 0.325 0.333 0.302
## [937] 0.327 0.317 0.337 0.323 0.318 0.305 0.288 0.315 0.293 0.286 0.286 0.328
## [949] 0.325 0.298 0.350 0.283 0.328 0.320 0.326 0.285 0.335 0.309 0.305 0.340
## [961] 0.331 0.321 0.338 0.325 0.311 0.293 0.332 0.288 0.293 0.298 0.284 0.314
## [973] 0.345 0.285 0.339 0.350 0.301 0.303 0.286 0.333 0.326 0.315 0.327 0.327
## [985] 0.325 0.345 0.299 0.327 0.329 0.335 0.297 0.312 0.298 0.330 0.346 0.293
## [997] 0.342 0.296 0.314 0.300 0.347 0.317 0.307 0.282 0.320 0.311 0.289 0.326
## [1009] 0.319 0.333 0.294 0.341 0.286 0.283 0.283 0.321 0.299 0.330 0.330 0.320
## [1021] 0.313 0.315 0.331 0.316 0.287 0.328 0.315 0.318 0.347 0.312 0.334 0.297
## [1033] 0.333 0.307 0.315 0.289 0.306 0.291 0.297 0.303 0.350 0.296 0.304 0.294
## [1045] 0.303 0.282 0.296 0.289 0.313 0.293 0.282 0.337 0.321 0.306 0.334 0.299
## [1057] 0.282 0.325 0.291 0.323 0.332 0.289 0.332 0.314 0.302 0.316 0.323 0.314
## [1069] 0.339 0.302 0.283 0.347 0.333 0.348 0.324 0.306 0.291 0.332 0.343 0.332
## [1081] 0.340 0.283 0.302 0.315 0.343 0.300 0.286 0.284 0.284 0.317 0.293 0.290
## [1093] 0.303 0.316 0.288 0.321 0.342 0.299 0.346 0.310 0.328 0.300 0.347 0.306
## [1105] 0.347 0.308 0.295 0.287 0.282 0.286 0.293 0.282 0.290 0.308 0.289 0.323
## [1117] 0.326 0.300 0.310 0.294 0.291 0.300 0.337 0.323 0.291 0.326 0.317 0.293
## [1129] 0.295 0.328 0.303 0.286 0.325 0.343 0.328 0.287 0.299 0.284 0.301 0.348
## [1141] 0.303 0.318 0.316 0.311 0.333 0.328 0.305 0.308 0.349 0.324 0.341 0.342
## [1153] 0.288 0.288 0.294 0.330 0.312 0.289 0.326 0.308 0.339 0.341 0.329 0.298
## [1165] 0.320 0.300 0.307 0.292 0.323 0.312 0.308 0.343 0.326 0.286 0.299 0.299
## [1177] 0.328 0.299 0.301 0.314 0.283 0.283 0.330 0.309 0.322 0.299 0.306 0.286
## [1189] 0.342 0.291 0.308 0.302 0.311 0.285 0.287 0.324 0.284 0.283 0.293 0.335
## [1201] 0.338 0.314 0.337 0.324 0.288 0.329 0.301 0.316 0.292 0.287 0.308 0.334
## [1213] 0.322 0.297 0.285 0.343 0.287 0.329 0.283 0.297 0.309 0.302 0.283 0.309
## [1225] 0.313 0.303 0.324 0.307 0.332 0.316 0.333 0.305 0.330 0.303 0.319 0.324
## [1237] 0.342 0.341 0.297 0.314 0.315 0.347 0.300 0.330 0.301 0.284 0.340 0.345
## [1249] 0.350 0.294 0.291 0.300 0.287 0.306 0.301 0.284 0.312 0.340 0.290 0.326
## [1261] 0.330 0.294 0.322 0.321 0.318 0.332 0.332 0.333 0.329 0.327 0.329 0.316
## [1273] 0.333 0.295 0.314 0.322 0.311 0.322 0.329 0.319 0.330 0.329 0.323 0.304
## [1285] 0.291 0.331 0.343 0.322 0.322 0.316 0.324 0.314 0.288 0.332 0.321 0.331
## [1297] 0.282 0.292 0.306 0.327 0.332 0.324 0.329 0.330 0.319 0.306 0.330 0.286
## [1309] 0.331 0.294 0.289 0.326 0.306 0.312 0.335 0.305 0.329 0.296 0.299 0.306
## [1321] 0.312 0.317 0.288 0.323 0.331 0.283 0.327 0.344 0.289 0.318 0.327 0.331
## [1333] 0.331 0.287 0.327 0.325 0.348 0.324 0.311 0.349 0.331 0.348 0.321 0.327
## [1345] 0.330 0.318 0.320 0.332 0.317 0.337 0.294 0.296 0.327 0.322 0.301 0.313
## [1357] 0.323 0.335 0.295 0.332 0.283 0.313 0.345 0.333 0.329 0.323 0.328 0.304
## [1369] 0.326 0.316 0.313 0.350 0.330 0.314 0.344 0.324 0.304 0.324 0.332 0.325
## [1381] 0.302 0.332 0.315 0.339 0.331 0.318 0.323 0.323 0.330 0.329 0.331 0.322
## [1393] 0.316 0.331 0.327 0.332 0.331 0.329 0.329 0.326 0.294 0.332 0.288 0.332
## [1405] 0.324 0.332 0.309 0.326 0.324 0.341 0.282 0.315 0.326 0.328 0.326 0.311
## [1417] 0.328 0.321 0.298 0.298 0.323 0.332 0.313 0.327 0.323 0.323 0.348 0.326
## [1429] 0.331 0.341 0.328 0.311 0.291 0.298 0.329 0.327 0.289 0.329 0.323 0.332
## [1441] 0.330 0.316 0.331 0.306 0.310 0.310 0.316 0.325 0.304 0.318 0.311 0.316
## [1453] 0.332 0.328 0.323 0.340 0.331 0.331 0.287 0.304 0.333 0.309 0.323 0.328
## [1465] 0.290 0.328 0.284 0.317 0.296 0.312 0.295 0.332 0.315 0.317 0.328 0.331
## [1477] 0.323 0.302 0.330 0.300 0.286 0.323 0.292 0.319 0.332 0.284 0.307 0.304
## [1489] 0.310 0.318 0.331 0.309 0.329 0.328 0.328 0.330 0.304 0.303 0.346 0.345
## [1501] 0.305 0.295 0.297 0.314 0.339 0.320 0.314 0.319 0.328 0.326 0.303 0.311
## [1513] 0.298 0.320 0.344 0.300 0.344 0.332 0.323 0.287 0.284 0.286 0.321 0.294
## [1525] 0.301 0.282 0.339 0.299 0.291 0.306 0.290 0.291 0.287 0.314 0.285 0.328
## [1537] 0.330 0.349 0.282 0.325 0.345 0.315 0.313 0.320 0.315 0.286 0.289 0.315
## [1549] 0.296 0.334 0.330 0.337 0.321 0.329 0.293 0.328 0.321 0.333 0.309 0.312
## [1561] 0.303 0.337 0.304 0.347 0.310 0.324 0.283 0.340 0.324 0.300 0.344 0.334
## [1573] 0.331 0.327 0.331 0.300 0.348 0.317 0.347 0.325 0.296 0.327 0.341 0.338
## [1585] 0.321 0.326 0.297 0.319 0.327 0.322 0.323 0.350 0.349 0.300 0.288 0.344
## [1597] 0.287 0.302 0.317 0.327 0.333 0.283 0.338 0.304 0.296 0.306 0.310 0.294
## [1609] 0.338 0.327 0.329 0.305 0.321 0.313 0.288 0.336 0.285 0.291 0.284 0.307
## [1621] 0.291 0.287 0.326 0.286 0.350 0.342 0.300 0.328 0.312 0.335 0.286 0.291
## [1633] 0.317 0.304 0.331 0.327 0.320 0.323 0.312 0.319 0.339 0.337 0.346 0.340
## [1645] 0.346 0.326 0.344 0.326 0.350 0.323 0.336 0.307 0.285 0.344 0.337 0.316
## [1657] 0.295 0.318 0.290 0.302 0.313 0.292 0.290 0.292 0.296 0.331 0.340 0.285
## [1669] 0.299 0.347 0.299 0.305 0.338 0.299 0.347 0.316 0.315 0.338 0.334 0.333
## [1681] 0.348 0.333 0.339 0.331 0.318 0.327 0.331 0.335 0.284 0.303 0.346 0.349
## [1693] 0.297 0.322 0.317 0.295 0.316 0.293 0.311 0.333 0.332 0.333 0.340 0.324
## [1705] 0.305 0.320 0.296 0.331 0.340 0.321 0.313 0.329 0.320 0.329 0.344 0.305
## [1717] 0.332 0.330 0.310 0.304 0.327 0.311 0.308 0.282 0.332 0.313 0.323 0.331
## [1729] 0.312 0.330 0.331 0.333 0.334 0.323 0.329 0.310 0.332 0.321 0.317 0.320
## [1741] 0.295 0.322 0.296 0.319 0.323 0.332 0.320 0.332 0.307 0.293 0.332 0.305
## [1753] 0.313 0.330 0.284 0.331 0.315 0.333 0.323 0.316 0.303 0.330 0.313 0.333
## [1765] 0.331 0.332 0.313 0.330 0.317 0.308 0.309 0.309 0.337 0.331 0.324 0.301
## [1777] 0.316 0.341 0.331 0.345 0.333 0.303 0.332 0.309 0.322 0.336 0.301 0.312
## [1789] 0.332 0.323 0.331 0.327 0.290 0.333 0.289 0.290 0.293 0.304 0.288 0.318
## [1801] 0.331 0.302 0.314 0.311 0.331 0.302 0.300 0.316 0.321 0.289 0.291 0.332
## [1813] 0.298 0.347 0.297 0.296 0.294 0.298 0.327 0.332 0.345 0.340 0.336 0.284
## [1825] 0.283 0.340 0.330 0.300 0.285 0.290 0.284 0.284 0.329 0.338 0.319 0.304
## [1837] 0.307 0.300 0.306 0.310 0.325 0.350 0.297 0.284 0.331 0.297 0.316 0.299
## [1849] 0.291 0.341 0.308 0.313 0.350 0.306 0.340 0.300 0.340 0.337 0.282 0.350
## [1861] 0.326 0.282 0.332 0.284 0.296 0.287 0.347 0.307 0.293 0.315 0.308 0.342
## [1873] 0.310 0.340 0.294 0.321 0.293 0.341 0.297 0.350 0.314 0.288 0.346 0.298
## [1885] 0.327 0.334 0.290 0.303 0.304 0.293 0.333 0.287 0.349 0.316 0.309 0.319
## [1897] 0.333 0.320 0.305 0.285 0.319 0.337 0.285 0.332 0.299 0.287 0.335 0.328
## [1909] 0.349 0.347 0.306 0.329 0.285 0.341 0.342 0.316 0.316 0.317 0.314 0.325
## [1921] 0.306 0.306 0.322 0.316 0.305 0.311 0.297 0.305 0.340 0.328rem_out2_kud50 <- subset(week_kuds, KUD50 < 0.281)
boxplot(rem_out2_kud50$KUD50, col = "green2", ylab="KUD50", main="Boxplot of KUD50 after the second outliers removal")
#Third removal
boxplot.stats(rem_out2_kud95$KUD95) #superior limit where outliers start is 1.311## $stats
## [1] 0.760 0.762 0.779 0.982 1.311
##
## $n
## [1] 21788
##
## $conf
## [1] 0.7766451 0.7813549
##
## $out
## [1] 1.385 1.433 1.362 1.383 1.454 1.469 1.427 1.430 1.373 1.349 1.478 1.334
## [13] 1.366 1.390 1.457 1.487 1.386 1.435 1.373 1.389 1.386 1.349 1.405 1.356
## [25] 1.323 1.403 1.488 1.453 1.423 1.325 1.407 1.392 1.353 1.328 1.354 1.332
## [37] 1.459 1.471 1.374 1.341 1.360 1.340 1.358 1.338 1.318 1.476 1.362 1.350
## [49] 1.378 1.391 1.432 1.491 1.356 1.359 1.358 1.316 1.341 1.413 1.431 1.420
## [61] 1.486 1.411 1.341 1.353 1.327 1.322 1.491 1.442 1.459 1.314 1.364 1.457
## [73] 1.441 1.413 1.405 1.448 1.327 1.469 1.413 1.428 1.462 1.422 1.468 1.380
## [85] 1.330 1.384 1.464 1.350 1.422 1.402 1.336 1.341 1.394 1.391 1.467 1.423
## [97] 1.448 1.339 1.381 1.462 1.414 1.419 1.463 1.355 1.385 1.348 1.415 1.320
## [109] 1.348 1.335 1.352 1.335 1.326 1.312 1.350 1.358 1.334 1.336 1.351 1.321
## [121] 1.394 1.312 1.335 1.456 1.492 1.415 1.459 1.397 1.344 1.328 1.328 1.445
## [133] 1.442 1.429 1.352 1.472 1.353 1.451 1.456 1.481 1.389 1.415 1.334 1.365
## [145] 1.361 1.421 1.368 1.387 1.425 1.353 1.477 1.329 1.489 1.318 1.363 1.384
## [157] 1.312 1.322 1.372 1.388 1.413 1.356 1.375 1.355 1.415 1.454 1.333 1.325
## [169] 1.411 1.375 1.452 1.376 1.368 1.468 1.431 1.345 1.414 1.414 1.345 1.488
## [181] 1.333 1.439 1.411 1.387 1.411 1.464 1.485 1.453 1.340 1.376 1.313 1.374
## [193] 1.420 1.319 1.398 1.482 1.411 1.475 1.378 1.325 1.482 1.357 1.483 1.411
## [205] 1.476 1.342 1.466 1.411 1.387 1.475 1.453 1.400 1.493 1.480 1.454 1.370
## [217] 1.388 1.442 1.445 1.381 1.461 1.400 1.326 1.423 1.473 1.463 1.339 1.477
## [229] 1.475 1.447 1.353 1.469 1.313 1.412 1.406 1.457 1.354 1.477 1.476 1.457
## [241] 1.461 1.386 1.457 1.339 1.490 1.411 1.488 1.493 1.427 1.383 1.338 1.458
## [253] 1.386 1.339 1.457 1.363 1.487 1.413 1.474 1.368 1.324 1.358 1.456 1.369
## [265] 1.470 1.453 1.474 1.314 1.381 1.401 1.313 1.344 1.455 1.374 1.455 1.440
## [277] 1.345 1.486 1.465 1.343 1.315 1.394 1.424 1.379 1.472 1.493 1.473 1.336
## [289] 1.393 1.403 1.314 1.357 1.440 1.386 1.464 1.430 1.359 1.417 1.378 1.461
## [301] 1.489 1.407 1.481 1.451 1.424 1.491 1.360 1.359 1.343 1.333 1.409 1.492
## [313] 1.377 1.409 1.350 1.346 1.337 1.468 1.362 1.361 1.453 1.482 1.435 1.387
## [325] 1.437 1.368 1.369 1.425 1.470 1.436 1.354 1.397 1.352 1.447 1.395 1.458
## [337] 1.486 1.433 1.397 1.408 1.474 1.396 1.316 1.391 1.322 1.473 1.407 1.353
## [349] 1.492 1.313 1.475 1.430 1.454 1.361 1.390 1.456 1.493 1.376 1.316 1.336
## [361] 1.490 1.349 1.312 1.335 1.410 1.480 1.361 1.433 1.413 1.450 1.350 1.475
## [373] 1.487 1.364 1.403 1.368 1.347 1.391 1.379 1.385 1.417 1.367 1.387 1.347
## [385] 1.389 1.398 1.397 1.320 1.341 1.460 1.432 1.328 1.358 1.386 1.368 1.380
## [397] 1.406 1.326 1.436 1.456 1.445 1.319 1.332 1.341 1.322 1.345 1.424 1.436
## [409] 1.334 1.367 1.422 1.459 1.367 1.421 1.459 1.431 1.332 1.358 1.328 1.320
## [421] 1.435 1.434 1.347 1.436 1.322 1.411 1.314 1.403 1.365 1.315 1.323 1.335
## [433] 1.354 1.378 1.386 1.319 1.323 1.396 1.347 1.452 1.379 1.415 1.432 1.386
## [445] 1.427 1.363 1.466 1.417 1.354 1.467 1.396 1.321 1.413 1.356 1.424 1.444
## [457] 1.337 1.482 1.410 1.319 1.435 1.365 1.353 1.349 1.354 1.327 1.400 1.374
## [469] 1.333 1.408 1.327 1.452 1.493 1.488 1.374 1.313 1.321 1.398 1.381 1.333
## [481] 1.386 1.486 1.412 1.323 1.331 1.326 1.355 1.397 1.381 1.393 1.329 1.486
## [493] 1.329 1.372 1.369 1.348 1.316 1.353 1.383 1.435 1.320 1.398 1.348 1.336
## [505] 1.404 1.483 1.478 1.313 1.368 1.447 1.470 1.321 1.316 1.340 1.457 1.316
## [517] 1.367 1.487 1.325 1.330 1.321 1.418 1.417 1.489 1.388 1.448 1.344 1.350
## [529] 1.410 1.317 1.464 1.458 1.361 1.376 1.438 1.317 1.414 1.448 1.316 1.329
## [541] 1.409 1.321 1.455 1.389 1.324 1.444 1.405 1.451 1.421 1.365 1.330 1.367
## [553] 1.486 1.469 1.376 1.328 1.342 1.404 1.312 1.313 1.362 1.354 1.347 1.332
## [565] 1.420 1.322 1.484 1.428 1.365 1.372 1.458 1.416 1.473 1.473 1.342 1.418
## [577] 1.363 1.402 1.428 1.423 1.451 1.369 1.368 1.401 1.350 1.338 1.445 1.317
## [589] 1.359 1.338 1.429 1.493 1.408 1.321 1.315 1.425 1.447 1.326 1.479 1.422
## [601] 1.477 1.363 1.442 1.400 1.365 1.379 1.352 1.331 1.467 1.328 1.373 1.491
## [613] 1.430 1.493 1.445 1.425 1.385 1.433 1.486 1.397 1.345 1.403 1.398 1.439
## [625] 1.378 1.362 1.370 1.319 1.404 1.422 1.390 1.398 1.416 1.466 1.446 1.402
## [637] 1.451 1.386 1.316 1.366 1.425 1.343 1.414 1.430 1.466 1.339 1.404 1.339
## [649] 1.486 1.443 1.317 1.362 1.389 1.386 1.403 1.347 1.428 1.315 1.396 1.415
## [661] 1.368 1.480 1.396 1.455 1.480 1.444 1.453 1.462 1.340 1.341 1.335 1.315
## [673] 1.319 1.405 1.327 1.379 1.363 1.372 1.487 1.361 1.348 1.315 1.367 1.410
## [685] 1.435 1.390 1.341 1.414 1.438 1.380 1.429 1.399 1.324 1.448 1.320 1.375
## [697] 1.410 1.474 1.410 1.431 1.412 1.475 1.454 1.370 1.415 1.371 1.359 1.453
## [709] 1.411 1.391 1.374 1.355 1.439 1.411 1.449 1.316 1.458 1.396 1.360 1.331
## [721] 1.421 1.387 1.365 1.312 1.458 1.318 1.434 1.390 1.352 1.366 1.464 1.319
## [733] 1.479 1.464 1.417 1.438 1.361 1.371 1.435 1.360 1.441 1.330 1.411 1.332
## [745] 1.404 1.329 1.336 1.313 1.375 1.375 1.312 1.491 1.373 1.376 1.380 1.342
## [757] 1.474 1.411 1.314 1.335 1.327 1.335 1.349 1.369 1.475 1.344 1.481 1.430
## [769] 1.343 1.406 1.337 1.316 1.380 1.432 1.431 1.477 1.456 1.412 1.398 1.484
## [781] 1.481 1.341 1.329 1.449 1.380 1.401 1.429 1.402 1.371 1.426 1.439 1.326
## [793] 1.327 1.441 1.460 1.462 1.437 1.414 1.493 1.335 1.374 1.344 1.344 1.484
## [805] 1.490 1.373 1.313 1.393 1.392 1.334 1.371 1.450 1.465 1.351 1.358 1.345
## [817] 1.361 1.397 1.383 1.438 1.471 1.485 1.376 1.481 1.439 1.473 1.462 1.390
## [829] 1.453 1.471 1.445 1.458 1.367 1.392 1.363 1.329 1.431 1.491 1.446 1.321
## [841] 1.393 1.466 1.376 1.432 1.341 1.416 1.324 1.483 1.469 1.483 1.476 1.339
## [853] 1.487 1.425 1.395 1.434 1.411 1.351 1.414 1.466 1.345 1.438 1.370 1.313
## [865] 1.396 1.489 1.492 1.325 1.327 1.447 1.458 1.485 1.419 1.479 1.484 1.410
## [877] 1.468 1.374 1.426 1.424 1.389 1.429 1.405 1.374 1.402 1.457 1.424 1.476
## [889] 1.325 1.392 1.343 1.463 1.321 1.449 1.467 1.425 1.475 1.373 1.438 1.411
## [901] 1.367 1.437 1.341 1.492 1.374 1.350 1.411 1.446 1.465 1.407 1.411 1.322
## [913] 1.401 1.387 1.315 1.475 1.404 1.368 1.380 1.396 1.372 1.375 1.419 1.475
## [925] 1.397 1.368 1.425 1.386 1.379 1.315 1.447 1.465 1.458 1.343 1.342 1.412
## [937] 1.427 1.447 1.366 1.411 1.383 1.411 1.325 1.392 1.404 1.394 1.395 1.459
## [949] 1.343 1.399 1.446 1.445 1.489 1.434 1.371 1.382 1.483 1.341 1.448 1.341
## [961] 1.392 1.470 1.474 1.448 1.467 1.491 1.386 1.386 1.389 1.426 1.357 1.335
## [973] 1.467 1.486 1.411 1.492 1.370 1.431 1.424 1.484 1.410 1.380 1.358 1.444
## [985] 1.343 1.474 1.473 1.470 1.417 1.373 1.373 1.417 1.420 1.430 1.383 1.341
## [997] 1.410 1.462 1.467 1.424 1.446 1.468 1.482 1.474 1.412 1.462 1.451 1.410
## [1009] 1.322 1.362 1.423 1.485 1.374 1.479 1.351 1.392 1.351 1.490 1.474 1.341
## [1021] 1.374 1.475 1.446 1.452 1.411 1.394 1.482 1.442 1.412 1.439 1.457 1.462
## [1033] 1.351 1.487 1.464 1.341 1.479 1.362 1.315 1.411 1.383 1.398 1.400 1.454
## [1045] 1.353 1.474 1.374 1.411 1.464 1.411 1.374 1.419 1.429 1.476 1.390 1.316
## [1057] 1.334 1.380 1.411 1.323 1.343 1.464 1.396 1.467 1.411 1.452 1.343 1.321
## [1069] 1.320 1.479 1.316 1.317 1.312 1.312 1.366 1.397 1.370 1.383 1.400 1.401
## [1081] 1.423 1.380 1.366 1.397 1.407 1.415 1.420 1.413 1.315 1.400 1.315 1.351
## [1093] 1.370 1.347 1.354 1.434 1.333 1.435 1.421 1.490 1.320 1.480 1.477 1.366
## [1105] 1.385 1.382 1.418 1.368 1.486 1.462 1.470 1.376 1.323 1.399 1.325 1.318
## [1117] 1.365 1.339 1.366 1.344 1.377 1.327 1.374 1.316 1.357 1.370 1.334 1.318
## [1129] 1.350 1.397 1.317 1.419 1.320 1.372 1.312 1.385 1.361 1.353 1.388 1.385
## [1141] 1.325 1.480 1.363 1.487 1.326 1.312 1.419 1.358 1.373 1.320 1.482 1.473
## [1153] 1.451 1.371 1.459 1.342 1.408 1.345 1.327 1.484 1.464 1.375 1.339 1.439
## [1165] 1.489 1.323 1.362 1.329 1.395 1.354 1.404 1.434 1.330 1.351 1.318 1.407
## [1177] 1.488 1.486 1.477 1.354 1.407 1.372 1.372 1.383 1.400 1.418 1.350 1.434
## [1189] 1.416 1.326 1.312 1.378 1.464 1.437 1.410 1.404 1.422 1.374 1.480 1.423
## [1201] 1.367 1.389 1.334 1.345 1.461 1.445 1.332 1.369 1.361 1.429 1.319 1.348
## [1213] 1.409 1.384 1.449 1.432 1.389 1.371 1.396 1.313 1.490 1.462 1.478 1.361
## [1225] 1.408 1.349 1.378 1.468 1.440 1.446 1.454 1.462 1.437 1.416 1.426 1.471
## [1237] 1.424 1.419 1.353 1.362 1.401 1.412 1.395 1.476 1.403 1.466 1.452 1.375
## [1249] 1.486 1.385 1.459 1.454 1.436 1.367 1.326 1.463 1.330 1.410 1.484 1.393
## [1261] 1.343 1.487 1.433 1.397 1.396 1.395 1.428 1.491 1.470 1.413 1.387 1.463
## [1273] 1.465 1.453 1.414 1.329 1.370 1.438 1.475 1.469 1.477 1.341 1.474 1.462
## [1285] 1.387 1.456 1.337 1.442 1.470 1.355 1.424 1.424 1.347 1.438 1.374 1.410
## [1297] 1.418 1.322 1.343 1.327 1.336 1.438 1.419 1.397 1.449 1.467 1.419 1.490
## [1309] 1.321 1.491 1.386 1.488 1.453 1.411 1.453 1.463 1.472 1.482 1.318 1.453
## [1321] 1.431 1.480 1.346 1.475 1.482 1.322 1.375 1.333 1.453 1.357 1.431 1.411
## [1333] 1.375 1.477 1.487 1.405 1.483 1.416 1.360 1.374 1.341 1.410 1.414 1.381
## [1345] 1.358 1.459 1.424 1.445 1.431 1.410 1.317 1.407 1.481 1.457 1.438 1.424
## [1357] 1.475 1.422 1.438 1.411 1.444 1.374 1.411 1.487 1.374 1.492 1.334 1.396
## [1369] 1.422 1.470 1.388 1.374 1.384 1.351 1.452 1.442 1.377 1.317 1.466 1.401
## [1381] 1.395 1.393 1.468 1.384 1.478 1.459 1.422 1.316 1.432 1.350 1.327 1.488
## [1393] 1.423 1.447 1.385 1.432 1.406 1.489 1.373 1.348 1.434 1.432 1.380 1.455
## [1405] 1.344 1.324 1.387 1.457 1.390 1.416 1.428 1.390 1.453 1.382 1.324 1.396
## [1417] 1.352 1.388 1.424 1.462 1.374 1.388 1.380 1.327 1.476 1.336 1.398 1.331
## [1429] 1.492 1.366 1.332 1.314 1.328 1.313 1.397 1.477 1.443 1.466 1.405 1.435
## [1441] 1.410 1.397 1.493 1.430 1.450 1.329 1.327 1.366 1.319 1.406 1.348 1.430
## [1453] 1.454 1.425 1.481 1.361 1.430 1.365 1.492 1.361 1.329 1.474 1.327 1.321
## [1465] 1.411 1.360 1.367 1.417 1.443 1.490 1.388 1.370 1.330 1.463 1.435rem_out3_kud95 <- subset(week_kuds, KUD95 < 1.311)
boxplot(rem_out3_kud95$KUD95, col = "deepskyblue", ylab="KUD95", main="Boxplot of KUD95 after the third outliers removal")
boxplot.stats(rem_out2_kud50$KUD50) #superior limit where outliers start is 0.233## $stats
## [1] 0.162 0.164 0.166 0.192 0.233
##
## $n
## [1] 21213
##
## $conf
## [1] 0.1656963 0.1663037
##
## $out
## [1] 0.245 0.251 0.256 0.241 0.255 0.274 0.270 0.262 0.273 0.240 0.277 0.243
## [13] 0.248 0.257 0.246 0.243 0.280 0.237 0.257 0.235 0.252 0.246 0.257 0.277
## [25] 0.250 0.249 0.266 0.279 0.273 0.276 0.266 0.251 0.272 0.270 0.243 0.245
## [37] 0.264 0.273 0.271 0.234 0.235 0.252 0.269 0.254 0.260 0.259 0.250 0.269
## [49] 0.252 0.245 0.237 0.277 0.256 0.279 0.278 0.258 0.246 0.240 0.245 0.245
## [61] 0.270 0.247 0.273 0.272 0.263 0.255 0.258 0.251 0.261 0.243 0.245 0.234
## [73] 0.257 0.259 0.260 0.275 0.234 0.265 0.248 0.234 0.269 0.235 0.249 0.279
## [85] 0.249 0.236 0.252 0.271 0.273 0.241 0.279 0.234 0.249 0.248 0.279 0.266
## [97] 0.251 0.276 0.261 0.239 0.271 0.271 0.247 0.234 0.242 0.265 0.249 0.253
## [109] 0.272 0.248 0.276 0.276 0.238 0.234 0.269 0.269 0.240 0.264 0.267 0.277
## [121] 0.242 0.241 0.240 0.252 0.276 0.271 0.275 0.250 0.263 0.261 0.252 0.270
## [133] 0.271 0.260 0.277 0.261 0.269 0.279 0.275 0.274 0.265 0.271 0.276 0.275
## [145] 0.273 0.276 0.275 0.236 0.236 0.262 0.278 0.272 0.242 0.265 0.252 0.259
## [157] 0.267 0.254 0.278 0.263 0.270 0.252 0.268 0.273 0.276 0.279 0.275 0.254
## [169] 0.270 0.255 0.239 0.260 0.251 0.252 0.251 0.251 0.280 0.245 0.244 0.255
## [181] 0.247 0.271 0.269 0.276 0.237 0.258 0.264 0.272 0.249 0.248 0.241 0.256
## [193] 0.244 0.247 0.274 0.265 0.264 0.242 0.259 0.252 0.268 0.279 0.242 0.269
## [205] 0.259 0.248 0.235 0.276 0.253 0.276 0.274 0.245 0.260 0.260 0.270 0.265
## [217] 0.260 0.265 0.251 0.271 0.253 0.260 0.245 0.235 0.258 0.237 0.278 0.265
## [229] 0.248 0.264 0.252 0.256 0.236 0.254 0.272 0.272 0.277 0.278 0.248 0.242
## [241] 0.274 0.271 0.247 0.270 0.252 0.242 0.258 0.271 0.258 0.258 0.236 0.275
## [253] 0.259 0.234 0.276 0.240 0.248 0.260 0.272 0.278 0.245 0.247 0.244 0.278
## [265] 0.280 0.246 0.273 0.257 0.268 0.262 0.278 0.276 0.239 0.245 0.253 0.268
## [277] 0.234 0.251 0.255 0.280 0.273 0.252 0.251 0.260 0.254 0.241 0.237 0.254
## [289] 0.249 0.262 0.246 0.236 0.236 0.277 0.268 0.249 0.238 0.256 0.246 0.250
## [301] 0.240 0.258 0.257 0.251 0.255 0.243 0.247 0.236 0.238 0.277 0.248 0.250
## [313] 0.272 0.238 0.254 0.235 0.237 0.234 0.277 0.274 0.243 0.272 0.277 0.252
## [325] 0.249 0.249 0.260 0.274 0.279 0.273 0.278 0.265 0.254 0.273 0.269 0.251
## [337] 0.267 0.237 0.235 0.253 0.273 0.273 0.252 0.254 0.254 0.259 0.263 0.268
## [349] 0.250 0.236 0.243 0.252 0.239 0.269 0.253 0.261 0.252 0.269 0.269 0.235
## [361] 0.272 0.265 0.251 0.244 0.272 0.251 0.259 0.248 0.268 0.257 0.254 0.267
## [373] 0.276 0.247 0.275 0.239 0.236 0.266 0.273 0.245 0.239 0.235 0.268 0.274
## [385] 0.245 0.257 0.266 0.236 0.259 0.248 0.245 0.274 0.256 0.244 0.270 0.239
## [397] 0.276 0.250 0.273 0.261 0.267 0.255 0.268 0.259 0.248 0.238 0.278 0.234
## [409] 0.259 0.247 0.244 0.278 0.274 0.278 0.279 0.264 0.251 0.238 0.239 0.238
## [421] 0.242 0.243 0.236 0.242 0.265 0.256 0.237 0.258 0.248 0.247 0.245 0.258
## [433] 0.259 0.265 0.274 0.248 0.243 0.253 0.240 0.234 0.260 0.239 0.269 0.242
## [445] 0.268 0.262 0.237 0.244 0.234 0.245 0.256 0.238 0.250 0.257 0.274 0.257
## [457] 0.261 0.248 0.268 0.262 0.262 0.274 0.250 0.279 0.254 0.235 0.262 0.245
## [469] 0.246 0.277 0.250 0.238 0.235 0.245 0.254 0.250 0.244 0.235 0.237 0.238
## [481] 0.241 0.242 0.243 0.237 0.235 0.257 0.270 0.245 0.277 0.249 0.263 0.271
## [493] 0.274 0.244 0.259 0.263 0.249 0.249 0.271 0.246 0.240 0.245 0.246 0.268
## [505] 0.265 0.243 0.263 0.268 0.239 0.271 0.244 0.238 0.256 0.259 0.260 0.273
## [517] 0.253 0.253 0.268 0.272 0.247 0.240 0.247 0.270 0.241 0.269 0.245 0.247
## [529] 0.240 0.248 0.261 0.240 0.241 0.249 0.244 0.240 0.240 0.255 0.238 0.236
## [541] 0.249 0.263 0.244 0.237 0.237 0.245 0.257 0.240 0.273 0.253 0.244 0.279
## [553] 0.240 0.239 0.255 0.241 0.248 0.257 0.234 0.239 0.280 0.245 0.251 0.248
## [565] 0.246 0.278 0.246 0.251 0.241 0.252 0.247 0.256 0.245 0.260 0.260 0.238
## [577] 0.251 0.252 0.269 0.275 0.272 0.261 0.236 0.246 0.256 0.247 0.237 0.242
## [589] 0.240 0.260 0.246 0.276 0.249 0.263 0.254 0.271 0.237 0.261 0.260 0.258
## [601] 0.249 0.249 0.260 0.249 0.235 0.249 0.234 0.256 0.260 0.253 0.276 0.270
## [613] 0.244 0.239 0.263 0.246 0.270 0.263 0.268 0.265 0.244 0.276 0.252 0.248
## [625] 0.249 0.259 0.263 0.247 0.269 0.236 0.266 0.254 0.272 0.252 0.254 0.259
## [637] 0.256 0.257 0.272 0.269 0.279 0.242 0.272 0.234 0.270 0.255 0.257 0.269
## [649] 0.241 0.262 0.250 0.261 0.262 0.252 0.258 0.241 0.262 0.240 0.249 0.244
## [661] 0.245 0.248 0.277 0.255 0.247 0.242 0.261 0.249 0.235 0.237 0.240 0.253
## [673] 0.238 0.235 0.243 0.241 0.238 0.245 0.274 0.251 0.234 0.242 0.239 0.254
## [685] 0.238 0.243 0.238 0.254 0.252 0.235 0.240 0.246 0.266 0.268 0.255 0.252
## [697] 0.253 0.264 0.242 0.238 0.247 0.258 0.245 0.280 0.243 0.236 0.249 0.237
## [709] 0.236 0.245 0.259 0.234 0.255 0.237 0.234 0.265 0.238 0.240 0.276 0.269
## [721] 0.248 0.252 0.259 0.260 0.256 0.270 0.277 0.246 0.253 0.235 0.235 0.270
## [733] 0.273 0.242 0.272 0.239 0.273 0.280 0.273 0.270 0.235 0.240 0.266 0.255
## [745] 0.276 0.248 0.249 0.268 0.248 0.259 0.240 0.238 0.265 0.269 0.255 0.244
## [757] 0.266 0.253 0.279 0.266 0.251 0.275 0.274 0.262 0.251 0.259 0.248 0.246
## [769] 0.237 0.240 0.248 0.253 0.238 0.234 0.249 0.246 0.249 0.252 0.236 0.234
## [781] 0.258 0.252 0.234 0.253 0.246 0.257 0.277 0.242 0.240 0.241 0.244 0.235
## [793] 0.252 0.254 0.251 0.249 0.254 0.258 0.251 0.244 0.247 0.234 0.248 0.254
## [805] 0.271 0.246 0.256 0.277 0.246 0.258 0.257 0.265 0.256 0.242 0.250 0.253
## [817] 0.238 0.247 0.255 0.245 0.245 0.238 0.237 0.238 0.247 0.273 0.250 0.241
## [829] 0.250 0.264 0.238 0.240 0.258 0.250 0.241 0.247 0.243 0.246 0.280 0.235
## [841] 0.266 0.279 0.264 0.249 0.238 0.273 0.262 0.265 0.270 0.256 0.267 0.264
## [853] 0.277 0.262 0.234 0.253 0.255 0.253 0.259 0.257 0.255 0.257 0.238 0.276
## [865] 0.274 0.278 0.262 0.272 0.277 0.243 0.264 0.270 0.236 0.241 0.239 0.255
## [877] 0.275 0.244 0.277 0.253 0.237 0.240 0.237 0.247 0.235 0.263 0.236 0.240
## [889] 0.259 0.248 0.274 0.259 0.234 0.280 0.238 0.257 0.260 0.234 0.249 0.265
## [901] 0.237 0.277 0.254 0.277 0.267 0.251 0.248 0.239 0.266 0.276 0.238 0.258
## [913] 0.243 0.266 0.276 0.263 0.262 0.265 0.248 0.267 0.244 0.244 0.257 0.271
## [925] 0.254 0.264 0.258 0.268 0.236 0.267 0.254 0.236 0.257 0.269 0.257 0.249
## [937] 0.258 0.243 0.237 0.243 0.245 0.249 0.250 0.234 0.264 0.265 0.255 0.234
## [949] 0.251 0.267 0.237 0.250 0.247 0.268 0.276 0.272 0.261 0.255 0.253 0.244
## [961] 0.244 0.250 0.256 0.244 0.279 0.279 0.246 0.279 0.276 0.249 0.275 0.247
## [973] 0.246 0.241 0.274 0.274 0.245 0.235 0.253 0.262 0.262 0.262 0.262 0.263
## [985] 0.268 0.238 0.273 0.237 0.263 0.247 0.262 0.242 0.273 0.271 0.243 0.267
## [997] 0.235 0.268 0.260 0.260 0.246 0.238 0.238 0.248 0.239 0.253 0.251 0.255
## [1009] 0.254 0.242 0.259 0.239 0.251 0.253 0.275 0.252 0.237 0.260 0.242 0.268
## [1021] 0.248 0.256 0.236 0.250 0.262 0.241 0.234 0.263 0.237 0.238 0.237 0.277
## [1033] 0.244 0.259 0.275 0.244 0.247 0.248 0.253 0.255 0.244 0.263 0.243 0.253
## [1045] 0.259 0.253 0.239 0.238 0.250 0.258 0.252 0.278 0.235 0.237 0.246 0.236
## [1057] 0.269 0.234 0.234 0.247 0.244 0.238 0.239 0.241 0.256 0.242 0.237 0.240
## [1069] 0.235 0.252 0.237 0.258 0.251 0.258 0.275 0.247 0.270 0.251 0.243 0.267
## [1081] 0.252 0.253 0.261 0.246 0.257 0.249 0.254 0.241 0.271 0.237 0.260 0.243
## [1093] 0.244 0.253 0.244 0.248 0.245 0.252 0.261 0.272 0.240 0.239 0.243 0.252
## [1105] 0.236 0.237 0.238 0.242 0.253 0.250 0.238 0.251 0.267 0.248 0.274 0.256
## [1117] 0.262 0.257 0.268 0.238 0.247 0.235 0.244 0.246 0.238 0.274 0.241 0.278
## [1129] 0.271 0.272 0.238 0.257 0.241 0.279 0.269 0.250 0.255 0.236 0.240 0.235
## [1141] 0.270 0.243 0.264 0.277 0.276 0.257 0.258 0.240 0.240 0.241 0.244 0.238
## [1153] 0.242 0.252 0.255 0.241 0.267 0.243 0.274 0.253 0.236 0.247 0.265 0.277
## [1165] 0.247 0.239 0.243 0.241 0.263 0.247 0.245 0.241 0.266 0.238 0.235 0.273
## [1177] 0.234 0.266 0.243 0.243 0.257 0.241 0.275 0.257 0.261 0.280 0.277 0.274
## [1189] 0.260 0.256 0.272 0.265 0.243 0.246 0.266 0.260 0.274 0.238 0.238 0.262
## [1201] 0.240 0.263 0.234 0.238 0.253 0.243 0.245 0.239 0.235 0.236 0.267 0.235
## [1213] 0.267 0.250 0.254 0.241 0.236 0.261 0.238 0.252 0.248 0.258 0.255 0.275
## [1225] 0.252 0.249 0.241 0.268 0.234 0.252 0.256 0.234 0.266 0.245 0.256 0.254
## [1237] 0.253 0.275 0.274 0.239 0.248 0.253 0.275 0.245 0.275 0.249 0.275 0.259
## [1249] 0.243 0.276 0.240 0.255 0.246 0.255 0.239 0.259 0.234 0.266 0.268 0.245
## [1261] 0.240 0.240 0.258 0.259 0.259 0.235 0.270 0.252 0.268 0.257 0.250 0.238
## [1273] 0.254 0.266 0.244 0.270 0.253 0.265 0.245 0.263 0.237 0.244 0.236 0.272
## [1285] 0.275 0.237 0.266 0.273 0.260 0.243 0.239 0.247 0.234 0.250 0.235 0.263
## [1297] 0.264 0.269 0.266 0.275 0.242 0.235 0.265 0.239 0.250 0.255 0.254 0.261
## [1309] 0.247 0.252 0.256 0.266 0.261 0.261 0.255 0.238 0.257 0.269 0.275 0.249
## [1321] 0.267 0.244 0.243 0.271 0.271 0.260 0.268 0.266 0.242 0.263 0.239 0.256
## [1333] 0.247 0.277 0.235 0.235 0.239 0.259 0.236 0.239 0.243 0.272 0.251 0.260
## [1345] 0.243 0.244 0.235 0.241 0.270 0.263 0.262 0.251 0.266 0.245 0.236 0.244
## [1357] 0.247 0.275 0.270 0.244 0.243 0.251 0.246 0.261 0.242 0.253 0.269 0.256
## [1369] 0.242 0.269 0.243 0.263 0.240 0.266 0.236 0.258 0.274 0.267 0.238 0.256
## [1381] 0.239 0.252 0.245 0.260 0.237 0.254 0.246 0.237 0.249 0.279 0.275 0.279
## [1393] 0.268 0.279 0.253 0.269 0.250 0.245 0.243 0.264 0.237 0.238 0.267 0.275
## [1405] 0.265 0.242 0.250 0.262 0.254 0.242 0.245 0.243 0.272 0.255 0.269 0.244
## [1417] 0.246 0.244 0.246 0.249 0.258 0.275 0.235 0.252 0.268 0.258 0.265 0.236
## [1429] 0.269 0.240 0.243 0.236 0.272 0.254 0.252 0.268 0.236 0.274 0.256 0.263
## [1441] 0.239 0.246 0.263 0.276 0.277 0.240 0.241 0.261 0.259 0.234 0.250 0.269
## [1453] 0.238 0.274 0.238 0.247 0.237 0.264 0.253 0.240 0.253 0.263 0.242 0.254
## [1465] 0.238 0.251 0.238 0.249 0.278 0.234 0.260 0.242 0.250 0.260 0.257 0.271
## [1477] 0.241 0.239 0.280 0.262 0.236 0.238 0.263 0.260 0.243 0.241 0.247 0.238
## [1489] 0.277 0.278 0.277 0.246 0.271 0.247 0.278 0.248 0.278 0.255 0.268 0.250
## [1501] 0.263 0.264 0.234 0.243 0.246 0.258 0.237 0.242 0.258 0.264 0.275 0.278
## [1513] 0.249 0.249 0.240 0.252 0.258 0.267 0.254 0.255 0.234 0.260 0.249 0.242
## [1525] 0.275 0.250 0.246 0.267 0.238 0.239 0.248 0.269 0.253 0.271 0.258 0.239
## [1537] 0.265 0.243 0.238 0.243 0.236 0.249 0.251 0.243 0.269 0.239 0.247 0.280
## [1549] 0.270 0.263 0.251 0.274 0.277 0.251 0.253 0.280 0.277 0.272 0.247 0.276
## [1561] 0.248 0.274 0.273 0.237 0.264 0.251 0.260 0.268 0.238 0.259 0.270 0.264
## [1573] 0.251 0.235 0.268 0.279 0.251 0.237 0.242 0.261 0.269 0.238 0.261 0.269
## [1585] 0.259 0.271 0.255 0.250 0.276 0.245 0.263 0.248 0.254 0.270 0.279 0.265
## [1597] 0.261 0.250 0.277 0.262 0.279 0.238 0.245 0.240 0.275 0.270 0.257 0.266
## [1609] 0.245 0.236 0.271 0.235 0.242 0.266 0.253 0.242 0.244 0.272 0.256 0.264
## [1621] 0.234 0.249 0.253 0.257 0.235 0.261 0.252 0.237 0.236 0.274 0.270 0.273
## [1633] 0.236 0.236 0.241 0.257 0.274 0.247 0.244 0.264 0.253 0.234 0.246 0.261
## [1645] 0.263 0.273 0.234 0.237 0.243 0.241 0.253 0.252 0.235 0.236 0.249 0.240
## [1657] 0.266 0.266 0.245 0.264 0.277 0.272 0.268 0.254 0.239 0.262 0.275 0.245
## [1669] 0.247 0.241 0.266 0.250 0.257 0.265 0.251 0.240 0.234 0.274 0.275 0.270
## [1681] 0.253 0.255 0.260 0.242 0.256 0.266 0.238 0.277 0.248 0.280 0.254 0.274
## [1693] 0.280 0.267 0.237 0.240 0.235 0.251 0.236 0.236 0.270 0.251 0.279 0.277
## [1705] 0.268 0.263 0.280 0.257 0.263 0.278 0.251 0.276 0.267 0.250 0.236 0.242
## [1717] 0.237 0.255 0.265 0.271 0.248 0.271 0.266 0.240 0.277 0.256 0.279 0.244
## [1729] 0.249 0.242 0.262 0.267 0.259 0.251 0.253 0.235 0.248 0.252 0.238 0.237
## [1741] 0.269 0.250 0.272 0.279 0.251 0.263 0.253 0.253 0.272 0.253 0.256 0.240
## [1753] 0.251 0.253 0.255 0.260 0.241 0.273 0.270 0.279 0.240 0.247 0.243 0.268
## [1765] 0.246 0.239 0.241 0.264 0.270 0.250 0.250 0.247 0.250 0.256 0.242 0.238
## [1777] 0.238 0.268 0.245 0.244 0.236 0.247 0.236 0.234 0.255 0.244 0.261 0.279
## [1789] 0.270 0.248 0.261 0.254 0.256 0.246 0.278 0.250 0.238 0.250 0.239 0.250
## [1801] 0.263 0.238 0.250 0.270 0.234 0.260 0.243 0.246 0.253 0.257 0.253 0.237
## [1813] 0.244 0.250 0.238 0.238 0.268 0.255 0.258 0.250 0.249 0.249 0.254 0.263
## [1825] 0.270 0.278 0.251 0.267 0.252 0.266 0.238 0.248 0.245 0.239 0.252 0.268
## [1837] 0.275 0.261 0.242 0.240 0.244 0.247 0.240 0.239 0.235 0.260 0.265 0.244
## [1849] 0.253 0.267 0.278 0.237 0.260 0.254 0.239 0.246 0.245 0.279 0.268 0.278
## [1861] 0.278 0.253 0.278 0.267 0.251 0.237 0.235 0.252 0.236 0.251 0.255 0.260
## [1873] 0.263 0.238 0.234 0.253 0.278 0.237 0.254 0.238 0.243 0.279 0.237 0.263
## [1885] 0.259 0.238 0.270 0.252 0.239 0.242 0.276 0.253 0.242 0.245 0.242 0.234
## [1897] 0.261 0.236 0.242 0.279 0.271 0.240 0.236 0.239 0.258 0.235 0.236 0.264
## [1909] 0.235 0.260 0.278 0.268 0.257 0.236 0.258 0.241 0.263 0.262 0.276 0.273
## [1921] 0.269 0.272 0.273 0.278 0.270 0.275 0.251 0.274 0.276 0.275 0.277 0.275
## [1933] 0.272 0.275 0.261 0.264 0.256 0.261 0.255 0.273 0.272 0.270 0.270 0.262
## [1945] 0.273 0.270 0.250 0.272 0.269 0.268 0.271 0.271 0.251 0.273 0.269 0.269
## [1957] 0.247 0.266 0.271 0.264 0.242 0.274 0.263 0.264 0.241 0.265 0.238 0.280
## [1969] 0.270 0.268 0.257 0.266 0.258 0.271 0.270 0.271 0.239 0.238 0.273 0.258
## [1981] 0.265 0.236 0.254 0.242 0.243 0.247 0.252 0.277 0.266 0.257 0.280 0.267
## [1993] 0.259 0.240 0.261 0.234 0.249 0.252 0.257 0.258 0.254 0.254 0.267 0.271
## [2005] 0.276 0.260 0.272 0.261 0.266 0.237 0.266 0.265 0.246 0.272 0.280 0.236
## [2017] 0.258 0.271 0.249 0.245 0.269 0.277 0.247 0.273 0.275 0.279 0.250 0.251
## [2029] 0.261 0.264 0.245 0.245 0.237 0.275 0.242 0.277 0.238 0.266 0.234 0.254
## [2041] 0.260 0.277 0.253 0.244 0.253 0.247 0.249 0.261 0.253 0.280 0.241 0.252
## [2053] 0.247 0.264 0.248 0.267 0.260 0.256 0.248 0.261 0.278 0.249 0.261 0.250
## [2065] 0.252 0.252 0.243 0.265 0.243 0.234 0.256 0.236 0.274 0.237 0.270 0.248
## [2077] 0.247 0.272 0.269 0.263 0.244 0.242 0.243 0.254 0.278 0.254 0.247 0.271
## [2089] 0.254 0.238 0.236 0.243 0.249 0.241 0.261 0.253 0.246 0.264 0.260 0.258
## [2101] 0.278 0.274 0.264 0.265 0.271 0.244 0.240 0.267 0.252 0.255 0.234 0.241
## [2113] 0.242 0.241 0.252 0.279 0.242 0.261 0.252 0.251 0.248 0.252 0.242 0.245
## [2125] 0.246 0.245 0.253 0.270 0.242 0.239 0.256 0.244 0.249 0.261 0.246 0.243
## [2137] 0.272 0.274 0.241 0.276 0.279 0.274 0.251 0.272 0.273 0.268 0.262 0.235
## [2149] 0.267 0.237 0.260 0.247 0.246 0.246 0.277 0.261 0.239 0.265 0.264 0.257
## [2161] 0.249 0.239 0.248 0.278 0.269 0.279 0.275 0.263 0.255 0.250 0.270 0.244
## [2173] 0.244 0.247 0.272 0.238 0.248 0.241 0.251 0.269 0.241 0.254 0.238 0.254
## [2185] 0.261 0.258 0.267 0.251 0.246 0.279 0.276 0.280 0.251 0.246 0.238 0.278
## [2197] 0.236 0.239 0.258 0.245 0.250 0.248 0.239 0.236 0.280 0.252 0.250 0.244
## [2209] 0.276 0.242 0.248 0.237 0.252 0.237 0.254 0.270 0.261 0.240 0.275 0.239
## [2221] 0.254 0.265 0.265 0.251 0.269 0.259 0.274 0.245 0.242 0.237 0.258 0.276
## [2233] 0.258 0.239 0.272 0.246 0.261 0.280 0.239 0.263 0.247 0.259 0.254 0.260
## [2245] 0.280 0.269 0.270 0.238 0.268 0.269 0.239 0.252 0.273 0.272 0.260 0.239
## [2257] 0.251 0.269 0.272 0.252 0.259 0.273 0.260 0.256 0.270 0.260 0.266 0.266
## [2269] 0.243 0.261 0.265 0.259 0.266 0.274 0.241 0.275 0.277 0.235 0.257 0.234
## [2281] 0.258 0.246 0.239 0.255 0.276 0.236 0.241 0.274 0.254 0.240 0.276 0.279
## [2293] 0.237 0.251 0.237 0.256 0.250 0.256 0.256 0.258 0.244 0.249 0.237 0.259
## [2305] 0.248 0.241 0.278 0.273 0.267 0.264 0.261 0.246 0.257 0.240 0.256 0.254
## [2317] 0.265 0.238 0.256 0.246 0.257 0.256 0.243 0.250 0.278 0.244 0.245 0.248
## [2329] 0.265 0.267 0.262 0.246 0.279rem_out3_kud50 <- subset(week_kuds, KUD50 < 0.233)
boxplot(rem_out3_kud50$KUD50, col = "green2", ylab="KUD50", main="Boxplot of KUD50 after the third outliers removal")
As the distributions of both KUD95 and KUD50 kept the outliers after consecutive removal, we decided to keep all observations in the dataset. This happened probably because the data has a high variability that is not explained by a normal distribution, so the outliers are not erros but in fact variability that the normal distribution can not explain but that hold relevant biological information that we should not ignore.
Linearity Assumption
KUD95
#Correlation between response and numeric explanatory variables
plot(week_kuds$LengthStd, week_kuds$KUD95, xlab = "Length Std", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Length Std")
plot(week_kuds$BodyMassStd, week_kuds$KUD95, xlab = "Body Mass Std", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Body Mass Std")
plot(week_kuds$Longevity, week_kuds$KUD95, xlab = "Longevity", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Longevity")
plot(week_kuds$Vulnerability, week_kuds$KUD95, xlab = "Vulnerability", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Vulnerability")
plot(week_kuds$Troph, week_kuds$KUD95, xlab = "Troph", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Troph")
plot(week_kuds$ReceiverDensity, week_kuds$KUD95, xlab = "Receiver Density", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Receiver Density")
plot(week_kuds$MonitArea_km2, week_kuds$KUD95, xlab = "MonitArea (km2)", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Monitored Area")
plot(week_kuds$MCP_km2, week_kuds$KUD95, xlab = "MCP (km2)", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of MCP")
plot(week_kuds$NReceivers, week_kuds$KUD95, xlab = "N Receivers", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of N Receivers")
plot(week_kuds$MaxDistReceivers, week_kuds$KUD95, xlab = "Max. Dist. Receivers", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Max. Dist. Receivers")
cor(week_kuds$LengthStd, week_kuds$KUD95)## [1] -0.02151777cor(week_kuds$BodyMassStd, week_kuds$KUD95)## [1] -0.0553196cor(week_kuds$Longevity, week_kuds$KUD95)## [1] -0.08065923cor(week_kuds$Vulnerability, week_kuds$KUD95)## [1] -0.04922472cor(week_kuds$Troph, week_kuds$KUD95)## [1] 0.06083729cor(week_kuds$ReceiverDensity, week_kuds$KUD95)## [1] -0.12147cor(week_kuds$MonitArea_km2, week_kuds$KUD95)## [1] 0.3207865cor(week_kuds$MCP_km2, week_kuds$KUD95)## [1] 0.0804618cor(week_kuds$NReceivers, week_kuds$KUD95)## [1] 0.3311147cor(week_kuds$MaxDistReceivers, week_kuds$KUD95)## [1] 0.1316282KUD50
#Correlation between response and numeric explanatory variables
plot(week_kuds$LengthStd, week_kuds$KUD50, xlab = "Length Std", ylab="KUD50", col="green2", main = "KUD50 as a function of LengthStd")
plot(week_kuds$BodyMassStd, week_kuds$KUD50, xlab = "Body Mass Std", ylab="KUD50", col="green2", main = "KUD50 as a function of Body Mass Std")
plot(week_kuds$Longevity, week_kuds$KUD50, xlab = "Longevity", ylab="KUD50", col="green2", main = "KUD50 as a function of Longevity")
plot(week_kuds$Vulnerability, week_kuds$KUD50, xlab = "Vulnerability", ylab="KUD50", col="green2", main = "KUD50 as a function of Vulnerability")
plot(week_kuds$Troph, week_kuds$KUD50, xlab = "Troph", ylab="KUD50", col="green2", main = "KUD50 as a function of Troph")
plot(week_kuds$ReceiverDensity, week_kuds$KUD50, xlab = "Receiver Density", ylab="KUD50", col="green2", main = "KUD50 as a function of Receiver Density")
plot(week_kuds$MonitArea_km2, week_kuds$KUD50, xlab = "MonitArea (km2)", ylab="KUD50", col="green2", main = "KUD50 as a function of Monitored Area")
plot(week_kuds$MCP_km2, week_kuds$KUD50, xlab = "MCP (km2)", ylab="KUD50", col="green2", main = "KUD50 as a function of MCP")
plot(week_kuds$NReceivers, week_kuds$KUD50, xlab = "N Receivers", ylab="KUD50", col="green2", main = "KUD50 as a function of N Receivers")
plot(week_kuds$MaxDistReceivers, week_kuds$KUD50, xlab = "Max. Dist. Receivers", ylab="KUD50", col="green2", main = "KUD50 as a function of Max. Dist. Receivers")
cor(week_kuds$LengthStd, week_kuds$KUD50)## [1] -0.01242211cor(week_kuds$BodyMassStd, week_kuds$KUD50)## [1] -0.04518465cor(week_kuds$Longevity, week_kuds$KUD50)## [1] -0.09251479cor(week_kuds$Vulnerability, week_kuds$KUD50)## [1] -0.08368454cor(week_kuds$Troph, week_kuds$KUD50)## [1] 0.03501519cor(week_kuds$ReceiverDensity, week_kuds$KUD50)## [1] -0.1059148cor(week_kuds$MonitArea_km2, week_kuds$KUD50)## [1] 0.313404cor(week_kuds$MCP_km2, week_kuds$KUD50)## [1] 0.06406506cor(week_kuds$NReceivers, week_kuds$KUD50)## [1] 0.3287046cor(week_kuds$MaxDistReceivers, week_kuds$KUD50)## [1] 0.1082455cor.data <- data.frame(week_kuds$KUD95, week_kuds$KUD50, week_kuds$LengthStd, week_kuds$BodyMassStd, week_kuds$Longevity, week_kuds$Vulnerability, week_kuds$Troph, week_kuds$ReceiverDensity, week_kuds$MonitArea_km2, week_kuds$MCP_km2, week_kuds$NReceivers, week_kuds$MaxDistReceivers)
names(cor.data) <- c("KUD95", "KUD50", "LengthStd", "BodyMassStd", "Longevity", "Vulnerability", "Troph", "ReceiverDensity", "MonitArea_km2", "MCP_km2", "NReceivers", "MaxDistReceivers")
cortable <- cor(cor.data)
corrplot(cortable, tl.col = "black")
It doesn’t seem to exist a linear relationship between the response variables and any of the numeric explanatory variables, since the correlation is approximately 0.
To see if makes sense to introduce a certain categorical variable in a linear model, we first need to observe if the different categories of each variable are easily distinguished by their response variable values.
#Boxplots of categorical explanatory variables KUD95
par(mfrow = c(2, 3))
boxplot(KUD95 ~ Habitat, data= week_kuds, col="deepskyblue", xlab="Habitat")
title("Boxplot of KUD95 given Habitat")
boxplot(KUD95 ~ Migration, data= week_kuds, col="deepskyblue", xlab = "Migration")
title("Boxplot of KUD95 given Migration")
boxplot(KUD95 ~ ComImport, data= week_kuds,col="deepskyblue", xlab = "Commercial Importance")
title("Boxplot of KUD95 given Commercial Importance")
leveneTest(KUD95 ~ Habitat, data = week_kuds)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 450.7 < 2.2e-16 ***
## 25609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(KUD95 ~ Migration, data = week_kuds)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 696.23 < 2.2e-16 ***
## 25610
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(KUD95 ~ ComImport, data = week_kuds)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 16.189 9.412e-08 ***
## 25609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Boxplots of categorical explanatory variables KUD50
par(mfrow = c(1, 3))
boxplot(KUD50 ~ Habitat, data= week_kuds, col="green2", xlab="Habitat")
title("Boxplot of KUD50 given Habitat")
boxplot(KUD50 ~ Migration, data= week_kuds, col="green2", xlab = "Migration")
title("Boxplot of KUD50 given Migration")
boxplot(KUD50 ~ ComImport, data= week_kuds,col="green2", xlab = "Commercial Importance")
title("Boxplot of KUD50 given Commercial Importance")
leveneTest(KUD50 ~ Habitat, data = week_kuds)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 283.08 < 2.2e-16 ***
## 25609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(KUD50 ~ Migration, data = week_kuds)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 402.53 < 2.2e-16 ***
## 25610
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(KUD50 ~ ComImport, data = week_kuds)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 9.7676 5.749e-05 ***
## 25609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1kruskal.test(KUD95 ~ Habitat, data = week_kuds)##
## Kruskal-Wallis rank sum test
##
## data: KUD95 by Habitat
## Kruskal-Wallis chi-squared = 722.95, df = 2, p-value < 2.2e-16kruskal.test(KUD95 ~ Migration, data = week_kuds)##
## Kruskal-Wallis rank sum test
##
## data: KUD95 by Migration
## Kruskal-Wallis chi-squared = 36.453, df = 1, p-value = 1.564e-09kruskal.test(KUD95 ~ ComImport, data = week_kuds)##
## Kruskal-Wallis rank sum test
##
## data: KUD95 by ComImport
## Kruskal-Wallis chi-squared = 327, df = 2, p-value < 2.2e-16We made boxplots to observe if the values of each category were different from each other, but the only thing we could see was that there was overlapping in the values of the boxes and a lot of outliers. Also, through the levene test we could observe that the variance between groups in each categorical variable is heterogeneous.
The data violates the Linearity Assumption.
Correlation Assumption
#Biological traits correlation
cor(week_kuds$LengthStd, week_kuds$BodyMassStd)## [1] 0.7489714cor(week_kuds$LengthStd, week_kuds$Longevity)## [1] -0.1539612cor(week_kuds$LengthStd, week_kuds$Vulnerability)## [1] -0.242292cor(week_kuds$LengthStd, week_kuds$Troph)## [1] -0.3042097cor(week_kuds$BodyMassStd, week_kuds$Longevity)## [1] -0.1734132cor(week_kuds$BodyMassStd, week_kuds$Vulnerability)## [1] -0.3117613cor(week_kuds$BodyMassStd, week_kuds$Troph)## [1] -0.1870678cor(week_kuds$Longevity, week_kuds$Vulnerability)## [1] 0.3942677cor(week_kuds$Longevity, week_kuds$Troph)## [1] 0.2072966cor(week_kuds$Vulnerability, week_kuds$Troph)## [1] 0.5659924plot(correlation(week_kuds[,c(11, 13, 14, 15, 16)], method = "pearson"), main="Linear correlation between biological traits")
cor.matrix1 <- data.frame(week_kuds$LengthStd, week_kuds$BodyMassStd, week_kuds$Longevity, week_kuds$Vulnerability, week_kuds$Troph)
names(cor.matrix1) <- c("LengthStd", "BodyMassStd", "Longevity", "Vulnerability", "Troph")
cortable1 <- cor(cor.matrix1)
corrplot(cortable1, tl.col = "black")
#Experimental design parameters correlation
cor(week_kuds$ReceiverDensity, week_kuds$MonitArea_km2)## [1] -0.3835498cor(week_kuds$ReceiverDensity, week_kuds$MCP_km2)## [1] -0.2094113cor(week_kuds$ReceiverDensity, week_kuds$NReceivers)## [1] -0.2748357cor(week_kuds$ReceiverDensity, week_kuds$MaxDistReceivers)## [1] -0.4524428cor(week_kuds$MonitArea_km2, week_kuds$MCP_km2)## [1] 0.4583335cor(week_kuds$MonitArea_km2, week_kuds$NReceivers)## [1] 0.9845062cor(week_kuds$MonitArea_km2, week_kuds$MaxDistReceivers)## [1] 0.7488135cor(week_kuds$MCP_km2, week_kuds$NReceivers)## [1] 0.4697432cor(week_kuds$MCP_km2, week_kuds$MaxDistReceivers)## [1] 0.5886984cor(week_kuds$NReceivers, week_kuds$MaxDistReceivers)## [1] 0.6589268plot(correlation(week_kuds[,c(17, 18, 19, 20, 21)]), main="Linear correlation between experimental design parameters")
cor.data2 <- data.frame(week_kuds$ReceiverDensity, week_kuds$MonitArea_km2, week_kuds$MCP_km2, week_kuds$NReceivers, week_kuds$MaxDistReceivers)
names(cor.data2) <- c("ReceiverDensity", "MonitArea_km2", "MCP_km2", "NReceivers", "MaxDistReceivers")
cortable2 <- cor(cor.data2)
corrplot(cortable2, tl.col = "black")
We can observe that the only biological traits with a relative high linear correlation are the length standardised (LenghtStd) with the body mass standardised (BodyMassStd), with 74,7%. For the experimental design parameters, the monitored area has a relative high linear correlation with the number of receivers (NReceivers) and with the maximum distance between receivers (MaxDistReceivers), with 98,4% and 74,4% respectively. In future possible linear model analysis we may choose to include only one of these variables to avoid errors in coefficient estimates.
Data Transformation
To the Response Variables
Log KUD95
plot(week_kuds$LengthStd, log(week_kuds$KUD95))
plot(week_kuds$BodyMassStd, log(week_kuds$KUD95))
plot(week_kuds$Longevity, log(week_kuds$KUD95))
plot(week_kuds$Vulnerability, log(week_kuds$KUD95))
plot(week_kuds$Troph, log(week_kuds$KUD95))
cor(week_kuds$LengthStd, log(week_kuds$KUD95))## [1] -0.0286699cor(week_kuds$BodyMass, log(week_kuds$KUD95))## [1] -0.06813146cor(week_kuds$Longevity, log(week_kuds$KUD95))## [1] -0.1182926cor(week_kuds$Vulnerability, log(week_kuds$KUD95))## [1] -0.08121546cor(week_kuds$Troph, log(week_kuds$KUD95))## [1] 0.05143071cor(week_kuds$ReceiverDensity, log(week_kuds$KUD95))## [1] -0.1443662cor(week_kuds$MonitArea_km2, log(week_kuds$KUD95))## [1] 0.3458998cor(week_kuds$MCP_km2, log(week_kuds$KUD95))## [1] 0.09336417cor(week_kuds$NReceivers, log(week_kuds$KUD95))## [1] 0.3627379cor(week_kuds$MaxDistReceivers, log(week_kuds$KUD95))## [1] 0.118242hist(log(week_kuds$KUD95))
qqnorm(log(week_kuds$KUD95))
qqline(log(week_kuds$KUD95), col = 2)
Log KUD50
plot(week_kuds$LengthStd, log(week_kuds$KUD50))
plot(week_kuds$BodyMassStd, log(week_kuds$KUD50))
plot(week_kuds$Longevity, log(week_kuds$KUD50))
plot(week_kuds$Vulnerability, log(week_kuds$KUD50))
plot(week_kuds$Troph, log(week_kuds$KUD50))
cor(week_kuds$LengthStd, log(week_kuds$KUD50))## [1] -0.0111613cor(week_kuds$BodyMass, log(week_kuds$KUD50))## [1] -0.07993413cor(week_kuds$Longevity, log(week_kuds$KUD50))## [1] -0.1187778cor(week_kuds$Vulnerability, log(week_kuds$KUD50))## [1] -0.1100756cor(week_kuds$Troph, log(week_kuds$KUD50))## [1] 0.0208178cor(week_kuds$ReceiverDensity, log(week_kuds$KUD50))## [1] -0.1158277cor(week_kuds$MonitArea_km2, log(week_kuds$KUD50))## [1] 0.3294109cor(week_kuds$MCP_km2, log(week_kuds$KUD50))## [1] 0.06668169cor(week_kuds$NReceivers, log(week_kuds$KUD50))## [1] 0.3509882cor(week_kuds$MaxDistReceivers, log(week_kuds$KUD50))## [1] 0.09256047hist(log(week_kuds$KUD50))
qqnorm(log(week_kuds$KUD50))
qqline(log(week_kuds$KUD50), col = 2)
The log transformation doesn’t resolve the problem of the Linearity Assumption.
x^2 KUD95
plot(week_kuds$LengthStd, (week_kuds$KUD95)^2)
plot(week_kuds$BodyMassStd, (week_kuds$KUD95)^2)
plot(week_kuds$Longevity, (week_kuds$KUD95)^2)
plot(week_kuds$Vulnerability, (week_kuds$KUD95)^2)
plot(week_kuds$Troph, (week_kuds$KUD95)^2)
cor(week_kuds$LengthStd, (week_kuds$KUD95)^2)## [1] -0.005002298cor(week_kuds$BodyMassStd, (week_kuds$KUD95)^2)## [1] -0.03391904cor(week_kuds$Longevity, (week_kuds$KUD95)^2)## [1] -0.04002724cor(week_kuds$Vulnerability, (week_kuds$KUD95)^2)## [1] -0.01940918cor(week_kuds$Troph, (week_kuds$KUD95)^2)## [1] 0.04631723cor(week_kuds$ReceiverDensity, (week_kuds$KUD95)^2)## [1] -0.0749169cor(week_kuds$MonitArea_km2, (week_kuds$KUD95)^2)## [1] 0.2242074cor(week_kuds$MCP_km2, (week_kuds$KUD95)^2)## [1] 0.04771912cor(week_kuds$NReceivers, (week_kuds$KUD95)^2)## [1] 0.2287884cor(week_kuds$MaxDistReceivers, (week_kuds$KUD95)^2)## [1] 0.1022885hist((week_kuds$KUD95)^2)
qqnorm((week_kuds$KUD95)^2)
qqline((week_kuds$KUD95)^2, col = 2)
x^2 KUD50
plot(week_kuds$LengthStd, (week_kuds$KUD50)^2)
plot(week_kuds$BodyMassStd, (week_kuds$KUD50)^2)
plot(week_kuds$Longevity, (week_kuds$KUD50)^2)
plot(week_kuds$Vulnerability, (week_kuds$KUD50)^2)
plot(week_kuds$Troph, (week_kuds$KUD50)^2)
cor(week_kuds$LengthStd, (week_kuds$KUD50)^2)## [1] -0.003901381cor(week_kuds$BodyMassStd, (week_kuds$KUD50)^2)## [1] -0.03280761cor(week_kuds$Longevity, (week_kuds$KUD50)^2)## [1] -0.05070559cor(week_kuds$Vulnerability, (week_kuds$KUD50)^2)## [1] -0.04537786cor(week_kuds$Troph, (week_kuds$KUD50)^2)## [1] 0.0283501cor(week_kuds$ReceiverDensity, (week_kuds$KUD50)^2)## [1] -0.06603929cor(week_kuds$MonitArea_km2, (week_kuds$KUD50)^2)## [1] 0.2099815cor(week_kuds$MCP_km2, (week_kuds$KUD50)^2)## [1] 0.04016722cor(week_kuds$NReceivers, (week_kuds$KUD50)^2)## [1] 0.2173508cor(week_kuds$MaxDistReceivers, (week_kuds$KUD50)^2)## [1] 0.08308354hist((week_kuds$KUD50)^2)
qqnorm((week_kuds$KUD50)^2)
qqline((week_kuds$KUD50)^2, col = 2)
The sqaured transformation doesn’t resolve the problem of the Linearity Assumption.
Squared root KUD95
plot(week_kuds$LengthStd, sqrt(week_kuds$KUD95))
plot(week_kuds$BodyMassStd, sqrt(week_kuds$KUD95))
plot(week_kuds$Longevity, sqrt(week_kuds$KUD95))
plot(week_kuds$Vulnerability, sqrt(week_kuds$KUD95))
plot(week_kuds$Troph, sqrt(week_kuds$KUD95))
cor(week_kuds$LengthStd, sqrt(week_kuds$KUD95))## [1] -0.02667786cor(week_kuds$BodyMass, sqrt(week_kuds$KUD95))## [1] -0.05735046cor(week_kuds$Longevity, sqrt(week_kuds$KUD95))## [1] -0.1018989cor(week_kuds$Vulnerability, sqrt(week_kuds$KUD95))## [1] -0.06670016cor(week_kuds$Troph, sqrt(week_kuds$KUD95))## [1] 0.0588293cor(week_kuds$ReceiverDensity, sqrt(week_kuds$KUD95))## [1] -0.1374409cor(week_kuds$MonitArea_km2, sqrt(week_kuds$KUD95))## [1] 0.343724cor(week_kuds$MCP_km2, sqrt(week_kuds$KUD95))## [1] 0.09047568cor(week_kuds$NReceivers, sqrt(week_kuds$KUD95))## [1] 0.3575089cor(week_kuds$MaxDistReceivers, sqrt(week_kuds$KUD95))## [1] 0.1298021hist(sqrt(week_kuds$KUD95))
qqnorm(sqrt(week_kuds$KUD95))
qqline(sqrt(week_kuds$KUD95), col = 2)
Squared root KUD50
plot(week_kuds$LengthStd, sqrt(week_kuds$KUD50))
plot(week_kuds$BodyMassStd, sqrt(week_kuds$KUD50))
plot(week_kuds$Longevity, sqrt(week_kuds$KUD50))
plot(week_kuds$Vulnerability, sqrt(week_kuds$KUD50))
plot(week_kuds$Troph, sqrt(week_kuds$KUD50))
cor(week_kuds$LengthStd, sqrt(week_kuds$KUD50))## [1] -0.01254113cor(week_kuds$BodyMass, sqrt(week_kuds$KUD50))## [1] -0.07007703cor(week_kuds$Longevity, sqrt(week_kuds$KUD50))## [1] -0.107971cor(week_kuds$Vulnerability, sqrt(week_kuds$KUD50))## [1] -0.09901242cor(week_kuds$Troph, sqrt(week_kuds$KUD50))## [1] 0.0292903cor(week_kuds$ReceiverDensity, sqrt(week_kuds$KUD50))## [1] -0.1141593cor(week_kuds$MonitArea_km2, sqrt(week_kuds$KUD50))## [1] 0.3296213cor(week_kuds$MCP_km2, sqrt(week_kuds$KUD50))## [1] 0.06758948cor(week_kuds$NReceivers, sqrt(week_kuds$KUD50))## [1] 0.3484063cor(week_kuds$MaxDistReceivers, sqrt(week_kuds$KUD50))## [1] 0.1035089hist(sqrt(week_kuds$KUD50))
qqnorm(sqrt(week_kuds$KUD50))
qqline(sqrt(week_kuds$KUD50), col = 2)
The squared root transformation doesn’t resolve the problem of the Linearity Assumption.
Inverse KUD95
plot(week_kuds$LengthStd, 1/week_kuds$KUD95)
plot(week_kuds$BodyMassStd, 1/week_kuds$KUD95)
plot(week_kuds$Longevity, 1/week_kuds$KUD95)
plot(week_kuds$Vulnerability, 1/week_kuds$KUD95)
plot(week_kuds$Troph, 1/week_kuds$KUD95)
cor(week_kuds$LengthStd, 1/week_kuds$KUD95)## [1] 0.02746866cor(week_kuds$BodyMass, 1/week_kuds$KUD95)## [1] 0.0836109cor(week_kuds$Longevity, 1/week_kuds$KUD95)## [1] 0.1362053cor(week_kuds$Vulnerability, 1/week_kuds$KUD95)## [1] 0.09961442cor(week_kuds$Troph, 1/week_kuds$KUD95)## [1] -0.0311552cor(week_kuds$ReceiverDensity, 1/(week_kuds$KUD95))## [1] 0.1407617cor(week_kuds$MonitArea_km2, 1/(week_kuds$KUD95))## [1] -0.321462cor(week_kuds$MCP_km2, 1/(week_kuds$KUD95))## [1] -0.08628076cor(week_kuds$NReceivers, 1/(week_kuds$KUD95))## [1] -0.3427128cor(week_kuds$MaxDistReceivers, 1/(week_kuds$KUD95))## [1] -0.08664873hist(1/week_kuds$KUD95)
qqnorm(1/week_kuds$KUD95)
qqline(1/week_kuds$KUD95, col = 2)
Inverse KUD50
plot(week_kuds$LengthStd, 1/week_kuds$KUD50)
plot(week_kuds$BodyMassStd, 1/week_kuds$KUD50)
plot(week_kuds$Longevity, 1/week_kuds$KUD50)
plot(week_kuds$Vulnerability, 1/week_kuds$KUD50)
plot(week_kuds$Troph, 1/week_kuds$KUD50)
cor(week_kuds$LengthStd, 1/week_kuds$KUD50)## [1] 0.007983447cor(week_kuds$BodyMass, 1/week_kuds$KUD50)## [1] 0.09435985cor(week_kuds$Longevity, 1/week_kuds$KUD50)## [1] 0.1309154cor(week_kuds$Vulnerability, 1/week_kuds$KUD50)## [1] 0.1230782cor(week_kuds$Troph, 1/week_kuds$KUD50)## [1] -0.003295783cor(week_kuds$ReceiverDensity, 1/(week_kuds$KUD50))## [1] 0.1093812cor(week_kuds$MonitArea_km2, 1/(week_kuds$KUD50))## [1] -0.3103144cor(week_kuds$MCP_km2, 1/(week_kuds$KUD50))## [1] -0.05909859cor(week_kuds$NReceivers, 1/(week_kuds$KUD50))## [1] -0.3358942cor(week_kuds$MaxDistReceivers, 1/(week_kuds$KUD50))## [1] -0.06677873hist(1/week_kuds$KUD50)
qqnorm(1/week_kuds$KUD50)
qqline(1/week_kuds$KUD50, col = 2)
The inverse transformation doesn’t resolve the problem of the Linearity Assumption.
Transformations to the response variables were made to try to meet the assumption of linearity and normality. However, we hadn’t any positive results and the data kept violating the assumptions.
To the Explanatory Variables
#KUD95
cor(log(week_kuds$KUD95), log(week_kuds$LengthStd))## [1] -0.01835412cor(log(week_kuds$KUD95), log(week_kuds$BodyMassStd))## [1] -0.04289739cor(log(week_kuds$KUD95),log(week_kuds$Longevity))## [1] -0.08026167cor(log(week_kuds$KUD95),log(week_kuds$Vulnerability))## [1] -0.07351917cor(log(week_kuds$KUD95),log(week_kuds$Troph))## [1] 0.04695362cor(log(week_kuds$ReceiverDensity), week_kuds$KUD95)## [1] -0.1164072cor(log(week_kuds$MonitArea_km2), week_kuds$KUD95)## [1] 0.2540448cor(log(week_kuds$MCP_km2), week_kuds$KUD95)## [1] 0.1903855cor(log(week_kuds$NReceivers), week_kuds$KUD95)## [1] 0.2427251cor(log(week_kuds$MaxDistReceivers), week_kuds$KUD95)## [1] 0.1725921cor(week_kuds$KUD50, log(week_kuds$LengthStd))## [1] -0.00651373cor(week_kuds$KUD50, log(week_kuds$BodyMassStd))## [1] -0.03474324cor(week_kuds$KUD50,log(week_kuds$Longevity))## [1] -0.06410422cor(week_kuds$KUD50,log(week_kuds$Vulnerability))## [1] -0.07578021cor(week_kuds$KUD50,log(week_kuds$Troph))## [1] 0.03158416cor(log(week_kuds$ReceiverDensity), week_kuds$KUD50)## [1] -0.07684298cor(log(week_kuds$MonitArea_km2), week_kuds$KUD50)## [1] 0.2564476cor(log(week_kuds$MCP_km2), week_kuds$KUD50)## [1] 0.1618219cor(log(week_kuds$NReceivers), week_kuds$KUD50)## [1] 0.2544955cor(log(week_kuds$MaxDistReceivers), week_kuds$KUD50)## [1] 0.144812cor(week_kuds$KUD95, week_kuds$LengthStd^2)## [1] -0.03205761cor(week_kuds$KUD95, week_kuds$BodyMassStd^2)## [1] -0.04682318cor(week_kuds$KUD95, week_kuds$Longevity^2)## [1] -0.09960863cor(week_kuds$KUD95, week_kuds$Vulnerability^2)## [1] -0.05235909cor(week_kuds$KUD95, week_kuds$Troph^2)## [1] 0.0654842cor(week_kuds$ReceiverDensity^2, week_kuds$KUD95)## [1] -0.08336138cor(week_kuds$MonitArea_km2^2, week_kuds$KUD95)## [1] 0.3544305cor(week_kuds$MCP_km2^2, week_kuds$KUD95)## [1] 0.0229182cor(week_kuds$NReceivers^2, week_kuds$KUD95)## [1] 0.3591757cor(week_kuds$MaxDistReceivers^2, week_kuds$KUD95)## [1] 0.07860331cor(week_kuds$KUD50, week_kuds$LengthStd^2)## [1] -0.01903804cor(week_kuds$KUD50, week_kuds$BodyMassStd^2)## [1] -0.03461579cor(week_kuds$KUD50, week_kuds$Longevity^2)## [1] -0.1127521cor(week_kuds$KUD50, week_kuds$Vulnerability^2)## [1] -0.08910187cor(week_kuds$KUD50, week_kuds$Troph^2)## [1] 0.03937493cor(week_kuds$ReceiverDensity^2, week_kuds$KUD50)## [1] -0.08135174cor(week_kuds$MonitArea_km2^2, week_kuds$KUD50)## [1] 0.3468013cor(week_kuds$MCP_km2^2, week_kuds$KUD50)## [1] 0.01081609cor(week_kuds$NReceivers^2, week_kuds$KUD50)## [1] 0.353728cor(week_kuds$MaxDistReceivers^2, week_kuds$KUD50)## [1] 0.05578804cor(week_kuds$KUD95, sqrt(week_kuds$LengthStd))## [1] -0.01508509cor(week_kuds$KUD95, sqrt(week_kuds$BodyMassStd))## [1] -0.04897952cor(week_kuds$KUD95, sqrt(week_kuds$Longevity))## [1] -0.06806335cor(week_kuds$KUD95, sqrt(week_kuds$Vulnerability))## [1] -0.04682171cor(week_kuds$KUD95, sqrt(week_kuds$Troph))## [1] 0.05879031cor(sqrt(week_kuds$ReceiverDensity), week_kuds$KUD95)## [1] -0.1378731cor(sqrt(week_kuds$MonitArea_km2), week_kuds$KUD95)## [1] 0.2912277cor(sqrt(week_kuds$MCP_km2), week_kuds$KUD95)## [1] 0.1567625cor(sqrt(week_kuds$NReceivers), week_kuds$KUD95)## [1] 0.2967655cor(sqrt(week_kuds$MaxDistReceivers), week_kuds$KUD95)## [1] 0.1564256cor(week_kuds$KUD50, sqrt(week_kuds$LengthStd))## [1] -0.009253797cor(week_kuds$KUD50, sqrt(week_kuds$BodyMassStd))## [1] -0.04507737cor(week_kuds$KUD50, sqrt(week_kuds$Longevity))## [1] -0.07897539cor(week_kuds$KUD50, sqrt(week_kuds$Vulnerability))## [1] -0.07999811cor(week_kuds$KUD50, sqrt(week_kuds$Troph))## [1] 0.03317919cor(sqrt(week_kuds$ReceiverDensity), week_kuds$KUD50)## [1] -0.1073464cor(sqrt(week_kuds$MonitArea_km2), week_kuds$KUD50)## [1] 0.2872584cor(sqrt(week_kuds$MCP_km2), week_kuds$KUD50)## [1] 0.1343301cor(sqrt(week_kuds$NReceivers), week_kuds$KUD50)## [1] 0.2999491cor(sqrt(week_kuds$MaxDistReceivers), week_kuds$KUD50)## [1] 0.1311348cor(week_kuds$KUD95, 1/week_kuds$LengthStd)## [1] -0.004437635cor(week_kuds$KUD95, 1/week_kuds$BodyMassStd)## [1] -0.01290895cor(week_kuds$KUD95, 1/week_kuds$Longevity)## [1] 0.02693019cor(week_kuds$KUD95, 1/week_kuds$Vulnerability)## [1] 0.0370628cor(week_kuds$KUD95, 1/week_kuds$Troph)## [1] -0.05382839cor(1/week_kuds$ReceiverDensity, week_kuds$KUD95)## [1] 0.0196011cor(1/week_kuds$MonitArea_km2, week_kuds$KUD95)## [1] -0.1869263cor(1/week_kuds$MCP_km2, week_kuds$KUD95)## [1] -0.1250668cor(1/week_kuds$NReceivers, week_kuds$KUD95)## [1] -0.1408403cor(1/week_kuds$MaxDistReceivers, week_kuds$KUD95)## [1] -0.1436929cor(week_kuds$KUD50, 1/week_kuds$LengthStd)## [1] 0.003311695cor(week_kuds$KUD50, 1/week_kuds$BodyMassStd)## [1] 0.01423426cor(week_kuds$KUD50, 1/week_kuds$Longevity)## [1] 0.03607618cor(week_kuds$KUD50, 1/week_kuds$Vulnerability)## [1] 0.06618764cor(week_kuds$KUD50, 1/week_kuds$Troph)## [1] -0.02913728cor(1/week_kuds$ReceiverDensity, week_kuds$KUD50)## [1] -0.005819776cor(1/week_kuds$MonitArea_km2, week_kuds$KUD50)## [1] -0.2036961cor(1/week_kuds$MCP_km2, week_kuds$KUD50)## [1] -0.1124298cor(1/week_kuds$NReceivers, week_kuds$KUD50)## [1] -0.1667151cor(1/week_kuds$MaxDistReceivers, week_kuds$KUD50)## [1] -0.123848Transformations to the independent variables were made to try to meet the assumption of linearity. However, we hadn’t any positive results and the data kept violating the assumption.
Modelling KUD95
We tested the full linear model and checked the assumptions.
#Full model assumption checking
lmtotal <- lm(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2, data = week_kuds)
summary(lmtotal)##
## Call:
## lm(formula = KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +
## Troph + Habitat + Migration + ComImport + ReceiverDensity +
## MonitArea_km2, data = week_kuds)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4379 -0.3405 -0.1232 0.1770 12.8982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2757879 0.0674894 4.086 4.39e-05 ***
## LengthStd 0.4682903 0.0550355 8.509 < 2e-16 ***
## BodyMassStd -0.4322197 0.0474916 -9.101 < 2e-16 ***
## Longevity -0.0061898 0.0004149 -14.918 < 2e-16 ***
## Vulnerability 0.0051100 0.0007503 6.811 9.92e-12 ***
## Troph 0.0069510 0.0166141 0.418 0.6757
## Habitatdemersal -0.1200547 0.0195780 -6.132 8.80e-10 ***
## Habitatpelagic-neritic 0.3613265 0.0216434 16.695 < 2e-16 ***
## Migrationoceanodromous 0.1313238 0.0193442 6.789 1.16e-11 ***
## ComImportmedium -0.1745732 0.0133959 -13.032 < 2e-16 ***
## ComImportminor 0.1102418 0.0523992 2.104 0.0354 *
## ReceiverDensity 0.0040995 0.0002647 15.485 < 2e-16 ***
## MonitArea_km2 0.0652505 0.0012001 54.369 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7087 on 25599 degrees of freedom
## Multiple R-squared: 0.1895, Adjusted R-squared: 0.1891
## F-statistic: 498.7 on 12 and 25599 DF, p-value: < 2.2e-16#Normality assumption
qqnorm(lmtotal$residuals)
qqline(lmtotal$residuals, col = 2)
#Homocedascity assumption
plot(lmtotal$fitted.values, lmtotal$residuals)
abline(h=0, col="red")
As expected, the linear model do not fit well to our data, since the assumptions are not met.
We tried to fit different types of models. We modeled the response against each predictor variable separately. We then compare the AIC of each model to see which one fitted better.
#Modeling (each variable separately)
#Length Standardised
lm_length <- lm(KUD95 ~ LengthStd, data=week_kuds)
glm_length <- glm(KUD95 ~ LengthStd, data=week_kuds, family=Gamma(link="log"))
gam_length <- gam(KUD95 ~ LengthStd, data=week_kuds, family=Gamma(link="log"))
glmmF_length <- glmmTMB(KUD95 ~ LengthStd + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_length <- glmmTMB(KUD95 ~ LengthStd + (1|Transmitter), data=week_kuds, family=Gamma(link="log")) #control = glmerControl(optimizer = "bobyqa")
glmmS_length <- glmmTMB(KUD95 ~ LengthStd + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_length <- gamm(KUD95 ~ s(LengthStd), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5#gamm4F_length <- gamm4(KUD95 ~ s(LengthStd), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_length <- gamm(KUD95 ~ s(LengthStd), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5## iteration 6## iteration 7#gamm4T_length <- gamm4(KUD95 ~ s(LengthStd), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_length <- gamm(KUD95 ~ s(LengthStd), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5#gamm4S_length <- gamm4(KUD95 ~ s(LengthStd), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_length, glm_length, gam_length, glmmF_length, glmmT_length, glmmS_length)## df AIC
## lm_length 3 60408.26
## glm_length 3 35354.67
## gam_length 3 42791.40
## glmmF_length 4 20408.20
## glmmT_length 4 10331.08
## glmmS_length 4 25565.58summary(gammF_length$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30060.72 30101.48 -15025.36
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.402132 1.402132 1.402132 1.402132 1.402132 1.402132 1.402132 1.402132
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.3753014 0.4326938
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.1079515 0.05499357 25563 1.962984 0.0497
## Xs(LengthStd)Fx1 -0.2165399 0.05370046 25563 -4.032365 0.0001
## Correlation:
## X(Int)
## Xs(LengthStd)Fx1 -0.001
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7310314 -0.4877050 -0.2048337 0.1705059 17.7859651
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_length$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 16441.62 16482.38 -8215.812
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5
## StdDev: 0.0003865896 0.0003865896 0.0003865896 0.0003865896 0.0003865896
## Xr6 Xr7 Xr8
## StdDev: 0.0003865896 0.0003865896 0.0003865896
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3775188 0.3164446
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.09778024 0.01355406 24761 7.214091 0.0000
## Xs(LengthStd)Fx1 0.01511383 0.01306119 24761 1.157155 0.2472
## Correlation:
## X(Int)
## Xs(LengthStd)Fx1 0.082
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.37337902 -0.43329724 -0.10739754 0.09135131 16.69617083
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_length$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 38088.52 38129.27 -19039.26
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.867361 1.867361 1.867361 1.867361 1.867361 1.867361 1.867361 1.867361
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3874701 0.5069951
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.14748060 0.07210376 25581 2.045394 0.0408
## Xs(LengthStd)Fx1 -0.07881835 0.06115692 25581 -1.288789 0.1975
## Correlation:
## X(Int)
## Xs(LengthStd)Fx1 0
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4751826 -0.4269536 -0.1770516 0.1560576 16.7949077
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#BodyMass Standardised
lm_bodymass <- lm(KUD95 ~ BodyMassStd, data=week_kuds)
glm_bodymass <- glm(KUD95 ~ BodyMassStd, data=week_kuds, family=Gamma(link="log"))
gam_bodymass <- gam(KUD95 ~ BodyMassStd, data=week_kuds, family=Gamma(link="log"))
glmmF_bodymass <- glmmTMB(KUD95 ~ BodyMassStd + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_bodymass <- glmmTMB(KUD95 ~ BodyMassStd + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_bodymass <- glmmTMB(KUD95 ~ BodyMassStd + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_bodymass <- gamm(KUD95 ~ s(BodyMassStd), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5#gamm4F_bodymass <- gamm4(KUD95 ~ s(BodyMassStd), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_bodymass <- gamm(KUD95 ~ s(BodyMassStd), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5## iteration 6## iteration 7#gamm4T_bodymass <- gamm4(KUD95 ~ s(BodyMassStd), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_bodymass <- gamm(KUD95 ~ s(BodyMassStd), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5#gamm4S_bodymass <- gamm4(KUD95 ~ s(BodyMassStd), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_bodymass, glm_bodymass, gam_bodymass, glmmF_bodymass, glmmT_bodymass, glmmS_bodymass)## df AIC
## lm_bodymass 3 60341.62
## glm_bodymass 3 35194.80
## gam_bodymass 3 42506.22
## glmmF_bodymass 4 20457.95
## glmmT_bodymass 4 10330.63
## glmmS_bodymass 4 25623.81summary(gammF_bodymass$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30157.54 30198.29 -15073.77
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.620637 1.620637 1.620637 1.620637 1.620637 1.620637 1.620637 1.620637
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.3736541 0.4334934
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.1045715 0.05476946 25563 1.909303 0.0562
## Xs(BodyMassStd)Fx1 -0.2814991 0.09460800 25563 -2.975426 0.0029
## Correlation:
## X(Int)
## Xs(BodyMassStd)Fx1 0.006
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7324494 -0.4964458 -0.2160497 0.1688127 18.1774451
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_bodymass$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 16438.86 16479.61 -8214.429
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5
## StdDev: 0.0002035433 0.0002035433 0.0002035433 0.0002035433 0.0002035433
## Xr6 Xr7 Xr8
## StdDev: 0.0002035433 0.0002035433 0.0002035433
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3762537 0.3164604
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.09498825 0.013493890 24761 7.039353 0.0000
## Xs(BodyMassStd)Fx1 0.01629013 0.009569512 24761 1.702295 0.0887
## Correlation:
## X(Int)
## Xs(BodyMassStd)Fx1 -0.064
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.37340997 -0.43360867 -0.10700779 0.09153392 16.68484562
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_bodymass$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 38618.86 38659.61 -19304.43
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.892624 1.892624 1.892624 1.892624 1.892624 1.892624 1.892624 1.892624
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3845178 0.5122769
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.1506280 0.07163738 25581 2.102645 0.0355
## Xs(BodyMassStd)Fx1 -0.3744871 0.11116730 25581 -3.368681 0.0008
## Correlation:
## X(Int)
## Xs(BodyMassStd)Fx1 0.008
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4710260 -0.4378846 -0.1981203 0.1411153 16.2151776
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#Longevity
lm_longevity <- lm(KUD95 ~ Longevity, data=week_kuds)
glm_longevity <- glm(KUD95 ~ Longevity, data=week_kuds, family=Gamma(link="log"))
gam_longevity <- gam(KUD95 ~ Longevity, data=week_kuds, family=Gamma(link="log"))
glmmF_longevity <- glmmTMB(KUD95 ~ Longevity + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_longevity <- glmmTMB(KUD95 ~ Longevity + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_longevity <- glmmTMB(KUD95 ~ Longevity + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_longevity <- gamm(KUD95 ~ s(Longevity), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_longevity <- gamm4(KUD95 ~ s(Longevity), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_longevity <- gamm(KUD95 ~ s(Longevity), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5## iteration 6## iteration 7#gamm4T_longevity <- gamm4(KUD95 ~ s(Longevity), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_longevity <- gamm(KUD95 ~ s(Longevity), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_longevity <- gamm4(KUD95 ~ s(Longevity), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_longevity, glm_longevity, gam_longevity, glmmF_longevity, glmmT_longevity, glmmS_longevity)## df AIC
## lm_longevity 3 60252.94
## glm_longevity 3 34951.62
## gam_longevity 3 42313.90
## glmmF_longevity 4 20491.38
## glmmT_longevity 4 10325.84
## glmmS_longevity 4 25626.29summary(gammF_longevity$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30657.31 30698.06 -15323.65
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.004994435 0.004994435 0.004994435 0.004994435 0.004994435 0.004994435
## Xr7 Xr8
## StdDev: 0.004994435 0.004994435
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.3628811 0.4381039
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.09897610 0.05442830 25564 1.818468 0.0690
## Xs(Longevity)Fx1 0.07136657 0.06098446 46 1.170242 0.2479
## Correlation:
## X(Int)
## Xs(Longevity)Fx1 -0.21
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7054544 -0.4904136 -0.1750471 0.1644542 18.5844194
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_longevity$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 16383.01 16423.76 -8186.505
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.747362 1.747362 1.747362 1.747362 1.747362 1.747362 1.747362 1.747362
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3568017 0.316441
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.0976919 0.01432626 24761 6.819078 0.0000
## Xs(Longevity)Fx1 -0.1317471 0.23120798 24761 -0.569821 0.5688
## Correlation:
## X(Int)
## Xs(Longevity)Fx1 0.032
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.37102553 -0.43691326 -0.10609358 0.09348865 16.73749753
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_longevity$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 38999.43 39040.18 -19494.71
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5
## StdDev: 0.0004036288 0.0004036288 0.0004036288 0.0004036288 0.0004036288
## Xr6 Xr7 Xr8
## StdDev: 0.0004036288 0.0004036288 0.0004036288
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3768419 0.5165025
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.13080732 0.07596754 25582 1.721884 0.0851
## Xs(Longevity)Fx1 0.08237376 0.09897957 28 0.832230 0.4123
## Correlation:
## X(Int)
## Xs(Longevity)Fx1 -0.383
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4456090 -0.4384991 -0.1606068 0.1335295 16.7637689
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#Vulnerability
lm_vulnerability <- lm(KUD95 ~ Vulnerability, data=week_kuds)
glm_vulnerability <- glm(KUD95 ~ Vulnerability, data=week_kuds, family=Gamma(link="log"))
gam_vulnerability <- gam(KUD95 ~ Vulnerability, data=week_kuds, family=Gamma(link="log"))
glmmF_vulnerability <- glmmTMB(KUD95 ~ Vulnerability + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_vulnerability <- glmmTMB(KUD95 ~ Vulnerability + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_vulnerability <- glmmTMB(KUD95 ~ Vulnerability + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_vulnerability <- gamm(KUD95 ~ s(Vulnerability), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_vulnerability <- gamm4(KUD95 ~ s(Vulnerability), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_vulnerability <- gamm(KUD95 ~ s(Vulnerability), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5## iteration 6## iteration 7#gamm4T_vulnerability <- gamm4(KUD95 ~ s(Vulnerability), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_vulnerability <- gamm(KUD95 ~ s(Vulnerability), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_vulnerability <- gamm4(KUD95 ~ s(Vulnerability), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_vulnerability, glm_vulnerability, gam_vulnerability, glmmF_vulnerability, glmmT_vulnerability, glmmS_vulnerability)## df AIC
## lm_vulnerability 3 60357.98
## glm_vulnerability 3 35242.26
## gam_vulnerability 3 42706.31
## glmmF_vulnerability 4 20491.43
## glmmT_vulnerability 4 10332.37
## glmmS_vulnerability 4 25624.76summary(gammF_vulnerability$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30654.78 30695.53 -15322.39
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.00204696 0.00204696 0.00204696 0.00204696 0.00204696 0.00204696
## Xr7 Xr8
## StdDev: 0.00204696 0.00204696
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.3631843 0.4380816
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.13813530 0.05767679 25564 2.394989 0.0166
## Xs(Vulnerability)Fx1 0.05038345 0.04332497 46 1.162919 0.2509
## Correlation:
## X(Int)
## Xs(Vulnerability)Fx1 0.384
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7052382 -0.4904786 -0.1745979 0.1643848 18.5889624
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_vulnerability$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 16440.07 16480.82 -8215.033
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5
## StdDev: 9.412236e-05 9.412236e-05 9.412236e-05 9.412236e-05 9.412236e-05
## Xr6 Xr7 Xr8
## StdDev: 9.412236e-05 9.412236e-05 9.412236e-05
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3764228 0.3164636
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.09368621 0.01367981 24761 6.848502 0.0000
## Xs(Vulnerability)Fx1 -0.01404752 0.01209911 24761 -1.161037 0.2456
## Correlation:
## X(Int)
## Xs(Vulnerability)Fx1 0.174
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.37336873 -0.43401081 -0.10727478 0.09191304 16.68618796
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_vulnerability$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 38995.83 39036.58 -19492.91
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.008092824 0.008092824 0.008092824 0.008092824 0.008092824 0.008092824
## Xr7 Xr8
## StdDev: 0.008092824 0.008092824
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3686247 0.5164789
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.21470748 0.07948504 25582 2.701231 0.0069
## Xs(Vulnerability)Fx1 0.07523367 0.05028284 28 1.496210 0.1458
## Correlation:
## X(Int)
## Xs(Vulnerability)Fx1 0.5
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4452047 -0.4386084 -0.1609825 0.1333185 16.7607449
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#Troph
lm_troph <- lm(KUD95 ~ Troph, data=week_kuds)
glm_troph <- glm(KUD95 ~ Troph, data=week_kuds, family=Gamma(link="log"))
gam_troph <- gam(KUD95 ~ Troph, data=week_kuds, family=Gamma(link="log"))
glmmF_troph <- glmmTMB(KUD95 ~ Troph + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_troph <- glmmTMB(KUD95 ~ Troph + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_troph <- glmmTMB(KUD95 ~ Troph + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_troph <- gamm(KUD95 ~ s(Troph), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) ##Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_troph <- gamm4(KUD95 ~ s(Troph), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_troph <- gamm(KUD95 ~ s(Troph), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5## iteration 6## iteration 7#gamm4T_troph <- gamm4(KUD95 ~ s(Troph), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_troph <- gamm(KUD95 ~ s(Troph), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_troph <- gamm4(KUD95 ~ s(Troph), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_troph, glm_troph, gam_troph, glmmF_troph, glmmT_troph, glmmS_troph)## df AIC
## lm_troph 3 60325.15
## glm_troph 3 35173.93
## gam_troph 3 42397.54
## glmmF_troph 4 20488.42
## glmmT_troph 4 10328.52
## glmmS_troph 4 25622.11summary(gammF_troph$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30654.85 30695.6 -15322.42
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.003022293 0.003022293 0.003022293 0.003022293 0.003022293 0.003022293
## Xr7 Xr8
## StdDev: 0.003022293 0.003022293
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.3514998 0.4381082
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.1407928 0.05328207 25564 2.642404 0.0082
## Xs(Troph)Fx1 0.1094933 0.05128460 46 2.135013 0.0381
## Correlation:
## X(Int)
## Xs(Troph)Fx1 0.25
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7057735 -0.4905407 -0.1743593 0.1641210 18.5869203
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_troph$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 16315.53 16356.28 -8152.763
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.515327 1.515327 1.515327 1.515327 1.515327 1.515327 1.515327 1.515327
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3407553 0.3164768
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.1015642 0.01384182 24761 7.337492 0e+00
## Xs(Troph)Fx1 0.9982972 0.28366773 24761 3.519248 4e-04
## Correlation:
## X(Int)
## Xs(Troph)Fx1 -0.171
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.37628411 -0.43727484 -0.10766155 0.09489305 16.50747430
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_troph$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 38996.26 39037.01 -19493.13
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.01037979 0.01037979 0.01037979 0.01037979 0.01037979 0.01037979
## Xr7 Xr8
## StdDev: 0.01037979 0.01037979
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3513253 0.5165112
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.1911215 0.06745823 25582 2.833183 0.0046
## Xs(Troph)Fx1 0.1380874 0.06046407 28 2.283793 0.0302
## Correlation:
## X(Int)
## Xs(Troph)Fx1 0.233
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4459125 -0.4386181 -0.1609980 0.1332556 16.7594377
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#Habitat
lm_habitat <- lm(KUD95 ~ Habitat, data=week_kuds)
glm_habitat <- glm(KUD95 ~ Habitat, data=week_kuds, family=Gamma(link="log"))
gam_habitat <- gam(KUD95 ~ Habitat, data=week_kuds, family=Gamma(link="log"))
glmmF_habitat <- glmmTMB(KUD95 ~ Habitat + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_habitat <- glmmTMB(KUD95 ~ Habitat + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_habitat <- glmmTMB(KUD95 ~ Habitat + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_habitat <- gamm(KUD95 ~ Habitat, random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_habitat <- gamm4(KUD95 ~ Habitat, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_habitat <- gamm(KUD95 ~ Habitat, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5## iteration 6## iteration 7## iteration 8#gamm4T_habitat <- gamm4(KUD95 ~ Habitat, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_habitat <- gamm(KUD95 ~ Habitat, random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_habitat <- gamm4(KUD95 ~ Habitat, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_habitat, glm_habitat, gam_habitat, glmmF_habitat, glmmT_habitat, glmmS_habitat)## df AIC
## lm_habitat 4 59545.84
## glm_habitat 4 33309.53
## gam_habitat 4 39499.58
## glmmF_habitat 5 20467.43
## glmmT_habitat 5 10242.59
## glmmS_habitat 5 25608.63summary(gammF_habitat$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30636.69 30677.44 -15313.34
##
## Random effects:
## Formula: ~1 | File
## (Intercept) Residual
## StdDev: 0.2754513 0.4381452
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.1760671 0.07221588 25564 2.438067 0.0148
## XHabitatdemersal -0.2361342 0.09064370 45 -2.605082 0.0124
## XHabitatpelagic-neritic 0.5137363 0.13455430 45 3.818059 0.0004
## Correlation:
## X(Int) XHbttd
## XHabitatdemersal -0.797
## XHabitatpelagic-neritic -0.537 0.428
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7063792 -0.4913755 -0.1750669 0.1649180 18.5981998
##
## Number of Observations: 25612
## Number of Groups: 48summary(gammT_habitat$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 16323.07 16363.82 -8156.535
##
## Random effects:
## Formula: ~1 | Transmitter
## (Intercept) Residual
## StdDev: 0.3549725 0.3162995
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.1343299 0.01855173 24761 7.240826 0
## XHabitatdemersal -0.1404723 0.02617063 24761 -5.367553 0
## XHabitatpelagic-neritic 0.3266083 0.04914857 848 6.645327 0
## Correlation:
## X(Int) XHbttd
## XHabitatdemersal -0.698
## XHabitatpelagic-neritic -0.377 0.263
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.37874372 -0.43561796 -0.10557924 0.09505689 16.50372555
##
## Number of Observations: 25612
## Number of Groups: 850summary(gammS_habitat$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 38977.48 39018.24 -19483.74
##
## Random effects:
## Formula: ~1 | Species
## (Intercept) Residual
## StdDev: 0.2703393 0.5164722
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.1549188 0.09159865 25582 1.691278 0.0908
## XHabitatdemersal -0.1894074 0.11578948 27 -1.635791 0.1135
## XHabitatpelagic-neritic 0.5659141 0.15318118 27 3.694410 0.0010
## Correlation:
## X(Int) XHbttd
## XHabitatdemersal -0.791
## XHabitatpelagic-neritic -0.598 0.473
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4465279 -0.4386535 -0.1607899 0.1336594 16.7629675
##
## Number of Observations: 25612
## Number of Groups: 30#Migration
lm_migration <- lm(KUD95 ~ Migration, data=week_kuds)
glm_migration <- glm(KUD95 ~ Migration, data=week_kuds, family=Gamma(link="log"))
gam_migration <- gam(KUD95 ~ Migration, data=week_kuds, family=Gamma(link="log"))
glmmF_migration <- glmmTMB(KUD95 ~ Migration + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_migration <- glmmTMB(KUD95 ~ Migration + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_migration <- glmmTMB(KUD95 ~ Migration + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_migration <- gamm(KUD95 ~ Migration, random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_migration <- gamm4(KUD95 ~ Migration, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_migration <- gamm(KUD95 ~ Migration, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5## iteration 6## iteration 7## iteration 8#gamm4T_migration <- gamm4(KUD95 ~ Migration, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_migration <- gamm(KUD95 ~ Migration, random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_migration <- gamm4(KUD95 ~ Migration, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_migration, glm_migration, gam_migration, glmmF_migration, glmmT_migration, glmmS_migration)## df AIC
## lm_migration 3 59783.05
## glm_migration 3 33916.45
## gam_migration 3 40261.07
## glmmF_migration 4 20480.58
## glmmT_migration 4 10304.26
## glmmS_migration 4 25616.83summary(gammF_migration$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30647.16 30679.77 -15319.58
##
## Random effects:
## Formula: ~1 | File
## (Intercept) Residual
## StdDev: 0.3232532 0.438126
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -0.0033103 0.0567591 25564 -0.058322 0.9535
## XMigrationoceanodromous 0.3872820 0.1039663 46 3.725073 0.0005
## Correlation:
## X(Int)
## XMigrationoceanodromous -0.546
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7055110 -0.4907120 -0.1752456 0.1643914 18.5908519
##
## Number of Observations: 25612
## Number of Groups: 48summary(gammT_migration$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 16400.94 16433.55 -8196.472
##
## Random effects:
## Formula: ~1 | Transmitter
## (Intercept) Residual
## StdDev: 0.3684918 0.3164387
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.03095838 0.01767476 24762 1.751558 0.0799
## XMigrationoceanodromous 0.14805088 0.02659811 848 5.566217 0.0000
## Correlation:
## X(Int)
## XMigrationoceanodromous -0.665
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.37391839 -0.43407999 -0.10603433 0.09325078 16.65302290
##
## Number of Observations: 25612
## Number of Groups: 850summary(gammS_migration$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 38982.97 39015.58 -19487.49
##
## Random effects:
## Formula: ~1 | Species
## (Intercept) Residual
## StdDev: 0.320519 0.5164504
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.0149861 0.07229769 25582 0.207284 0.8358
## XMigrationoceanodromous 0.4497332 0.12966586 28 3.468401 0.0017
## Correlation:
## X(Int)
## XMigrationoceanodromous -0.558
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4458140 -0.4389911 -0.1607780 0.1337875 16.7638686
##
## Number of Observations: 25612
## Number of Groups: 30#ComImport
lm_comimport <- lm(KUD95 ~ ComImport, data=week_kuds)
glm_comimport <- glm(KUD95 ~ ComImport, data=week_kuds, family=Gamma(link="log"))
gam_comimport <- gam(KUD95 ~ ComImport, data=week_kuds, family=Gamma(link="log"))
glmmF_comimport <- glmmTMB(KUD95 ~ ComImport + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_comimport <- glmmTMB(KUD95 ~ ComImport + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_comimport <- glmmTMB(KUD95 ~ ComImport + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_comimport <- gamm(KUD95 ~ ComImport, random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_comimport <- gamm4(KUD95 ~ ComImport, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_comimport <- gamm(KUD95 ~ ComImport, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4## iteration 5## iteration 6## iteration 7#gamm4T_comimport <- gamm4(KUD95 ~ ComImport, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_comimport <- gamm(KUD95 ~ ComImport, random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_comimport <- gamm4(KUD95 ~ ComImport, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_comimport, glm_comimport, gam_comimport, glmmF_comimport, glmmT_comimport, glmmS_comimport)## df AIC
## lm_comimport 4 60400.97
## glm_comimport 4 35333.69
## gam_comimport 4 42658.62
## glmmF_comimport 5 20492.95
## glmmT_comimport 5 10318.17
## glmmS_comimport 5 25627.65summary(gammF_comimport$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30655.11 30695.86 -15322.55
##
## Random effects:
## Formula: ~1 | File
## (Intercept) Residual
## StdDev: 0.3613773 0.4380884
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.08066130 0.07402136 25564 1.0897031 0.2759
## XComImportmedium 0.12114671 0.11581658 45 1.0460222 0.3011
## XComImportminor -0.08389121 0.16293504 45 -0.5148752 0.6092
## Correlation:
## X(Int) XCmImprtmd
## XComImportmedium -0.639
## XComImportminor -0.454 0.290
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7050327 -0.4904828 -0.1744788 0.1642765 18.5872554
##
## Number of Observations: 25612
## Number of Groups: 48summary(gammT_comimport$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 16435.14 16475.89 -8212.569
##
## Random effects:
## Formula: ~1 | Transmitter
## (Intercept) Residual
## StdDev: 0.3754841 0.3164571
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.10330239 0.01543433 24760 6.693025 0.0000
## XComImportmedium -0.00045608 0.03133734 24760 -0.014554 0.9884
## XComImportminor -0.18577454 0.04724841 24760 -3.931869 0.0001
## Correlation:
## X(Int) XCmImprtmd
## XComImportmedium -0.479
## XComImportminor -0.144 0.069
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.37336109 -0.43452606 -0.10750912 0.09002168 16.68731015
##
## Number of Observations: 25612
## Number of Groups: 850summary(gammS_comimport$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 38995.75 39036.51 -19492.88
##
## Random effects:
## Formula: ~1 | Species
## (Intercept) Residual
## StdDev: 0.372644 0.516472
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) 0.19053859 0.1180325 25582 1.6142891 0.1065
## XComImportmedium 0.01205211 0.1573842 27 0.0765776 0.9395
## XComImportminor -0.19545923 0.1922476 27 -1.0167058 0.3183
## Correlation:
## X(Int) XCmImprtmd
## XComImportmedium -0.750
## XComImportminor -0.614 0.460
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.4454682 -0.4385825 -0.1608901 0.1334954 16.7619480
##
## Number of Observations: 25612
## Number of Groups: 30Observing the AIC values of all models adjusted, we can see that the one that better fits the data is the GLMM, with the Transmitter as random effect. Looking for all the GLMM adjusted, with higher attention to the variables used as random effects, we see that the models adjusted with Transmitter are better than the ones adjusted with File, and lastly Species.
To analyse and support the importance of each variable assigned to the random effect, we also perform a random forest model, including all three variables: Transmitter, File and Species.
random_three <- rpart(KUD95 ~ Species + Transmitter + File, data = week_kuds)
summary(random_three)## Call:
## rpart(formula = KUD95 ~ Species + Transmitter + File, data = week_kuds)
## n= 25612
##
## CP nsplit rel error xerror xstd
## 1 0.33819203 0 1.0000000 1.0000517 0.04300715
## 2 0.04950932 1 0.6618080 0.6882851 0.03105539
## 3 0.04599746 2 0.6122986 0.6295822 0.02954460
## 4 0.01900331 3 0.5663012 0.6062762 0.02899443
## 5 0.01000000 4 0.5472979 0.5880364 0.02788524
##
## Variable importance
## Transmitter File Species
## 64 24 12
##
## Node number 1: 25612 observations, complexity param=0.338192
## mean=1.123676, MSE=0.6193676
## left son=2 (23133 obs) right son=3 (2479 obs)
## Primary splits:
## Transmitter splits as LLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLRLLLLLLRLLLLRLRRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRRRRRRRRLRRLRLRLRRLLRRRRLLRLRLLRRRRRRRRLLLRRRRRRRLLRLRLLLLLLRRLLLRLLRLRRRRRRLLLLLRLLLLLLLLRRLLLRLLLLLLLLLLLLRLRLLLLLLRLLLRLRRLLLLLLLLLLLRRLLLLLLLLLLLLLLLLLLRRLLRRRLLLLLLRLRLLLLLLLRLLLLLLLLLLLLRRLLLLLLRLLLLLLLLLLLLLLLLLLLRRLRLLLLRRLLRLRRRLLLRLLLRRLLLRLLLRLLLLLLRLRRRLLLLLRLLRLLLLRRRRLRRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRRL, improve=0.3381920, (0 missing)
## File splits as LLLLRLLLLLLLLLLLLLLLLLRLLLLRLLLLLLRLLLLLLLLLRLLL, improve=0.2195713, (0 missing)
## Species splits as RRRLRLLLRLLLLLRLRLLLLRLLLLLRLL, improve=0.1174541, (0 missing)
## Surrogate splits:
## File splits as LLLLRLLLLLLLLLLLLLLLLLRLLLLRLLLLLLRLLLLLLLLLRLLL, agree=0.944, adj=0.416, (0 split)
## Species splits as LLLLLLLLRLLLLLRLLLLLLRLLLLLLLL, agree=0.920, adj=0.172, (0 split)
##
## Node number 2: 23133 observations, complexity param=0.04950932
## mean=0.9738526, MSE=0.141553
## left son=4 (17858 obs) right son=5 (5275 obs)
## Primary splits:
## Transmitter splits as LLLLLLLLLLLLLLLLLLLLLLLLRLLLR-LLRRRRLLRRLLLLLLRLLRRLLLLRLLLLRRLLRRLLLLRRLRRLRRRRRL-LLLLRRLLLRLLLLRLLRLLLLLLLLRLLLLRLRRLLLLLLRLLLRLLLRLLLRLLLLLLLLLRLRLLRLLLL-RLLLLLLLLRLLLRLRRLRRRLRLRLLLLRLRLRLRLRLLRLLLLLLLLLLLLLLLLLRRLLLLLLLRLLLLLLLLLLLRRLLLLLLLLLLLLLLLLLRLLLLLLRRLR-RLLRLL-LRLLLL-LRLR-R--LLLRLLLLLRLLLLLLLRLLLLLLLLLLLLLLLLLLLLRLLLRLL-R-LLLRLRRRLLRLLLLRRRRLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL--------R--R-L-R--RL----LR-L-LL--------RLR-------LL-L-LLLRLL--LLL-RR-L------LRRLR-LRLRLRLR--LRR-RRLLLLLLLLLR-R-LLLLLR-LLL-R--LRLLRRLLLRR--LLLLLLLLLLLLRRLLRR--LL---RLLLLR-R-LLLLRRR-RRLLRRLLRRRR--LRRRRR-RLLLLRRLLLLLLRRRRRR--L-LLRL--LL-R---RLR-LRL--RRR-RLL-RRRLLR-R---RRRLL-LL-LLLL----L--RLRLLLLLRLRLLLRLLRLLRRRLLLRRLRRLRLLRL-RLLRRRRRLRRLRLRLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLL-LLRRLLLLLLRRLRLLRRRLRLLRLLLLLLL--L, improve=0.23984350, (0 missing)
## File splits as RRRRRRLLLLLLLRLLLLRLLL-RLLLRRLRLLLRLLLLLLLRLRRLL, improve=0.06895091, (0 missing)
## Species splits as RRRLRLLR-LRRLLRLRLRLLRLLLLLLRL, improve=0.05248863, (0 missing)
## Surrogate splits:
## File splits as RRRLLRLLLLLLLLLLLLLLLL-RLLLRLLRLLLRLLLLLLLRLRLLL, agree=0.803, adj=0.135, (0 split)
## Species splits as RRLLRLLL-LRLLLRLLLLLLRLLLLLLLL, agree=0.796, adj=0.104, (0 split)
##
## Node number 3: 2479 observations, complexity param=0.04599746
## mean=2.521761, MSE=2.914028
## left son=6 (1303 obs) right son=7 (1176 obs)
## Primary splits:
## Transmitter splits as -----------------------------L----------------------------------------------------L-------------------------------------------------------------------------L-------------------------------------------------------------------------------------------------------------L------R------R----L-RL---------------------------------------------R-R---------------------------------------------------------------------------------RRRRLLRR-LL-L-R-RL--RRRR--R-R--LRRRRRRL---LRRLRLL--L-R------LR---L--R-LRRLRL-----R--------LL---L------------R-L------R---L-LL-----------LL------------------LL--LLL------L-L-------L------------LL------L-------------------RL-L----LR--L-LRL---L---RL---L---R------R-RRL-----L--R----LRRL-RR-------------------------------------L--------------------------------------------------------------------------L-------------------------------LL-, improve=0.10100800, (0 missing)
## File splits as --LLRLL---LL-L---RLL--R-L-RRL-----R----L--L-R---, improve=0.03657201, (0 missing)
## Species splits as LLRL-LRLR--LLRR-L----R---L-R--, improve=0.03151720, (0 missing)
## Surrogate splits:
## File splits as --LLRLL---LL-L---LLL--R-L-RRL-----R----L--L-R---, agree=0.743, adj=0.457, (0 split)
## Species splits as LLLL-LLLR--LLRR-L----R---L-R--, agree=0.725, adj=0.421, (0 split)
##
## Node number 4: 17858 observations
## mean=0.8737102, MSE=0.05085055
##
## Node number 5: 5275 observations
## mean=1.312875, MSE=0.2997303
##
## Node number 6: 1303 observations
## mean=2.006347, MSE=1.278305
##
## Node number 7: 1176 observations, complexity param=0.01900331
## mean=3.092836, MSE=4.105932
## left son=14 (970 obs) right son=15 (206 obs)
## Primary splits:
## Transmitter splits as ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------L------L------L----------------------------------------------L-L---------------------------------------------------------------------------------LRRR--RR------R-L---RLLL--R-L---LLRRLL-----LL-R------R-------R------L--LL-L------L--------------------------L--------L------------------------------------------------------------------------------------------------------R--------L-----L--------R--------L------R-RL---------L-----LR--LL---------------------------------------------------------------------------------------------------------------------------------------------------, improve=0.06243126, (0 missing)
## Species splits as -LR---L-R--L-RR-R----L-----L--, improve=0.02460779, (0 missing)
## File splits as --L-R------------L----R---RRL-----L-------R-L---, improve=0.02460779, (0 missing)
## Surrogate splits:
## Species splits as -LL---L-R--L-LL-R----L-----L--, agree=0.86, adj=0.199, (0 split)
## File splits as --L-L------------L----R---LLL-----L-------R-L---, agree=0.86, adj=0.199, (0 split)
##
## Node number 14: 970 observations
## mean=2.859514, MSE=3.533638
##
## Node number 15: 206 observations
## mean=4.191485, MSE=5.337343As suspected by the AIC of the previous models, the Transmitter has higher importance, followed by the File and then the Species.
To choose which one of these variables include in the model, we tested all possible combinations.
glmmT_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmF_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Species), data=week_kuds, family=Gamma(link="log"))
glmmTF_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmTS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log"))
glmmFS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))
glmmTFS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))
AIC(glmmT_total, glmmF_total, glmmS_total, glmmTF_total, glmmTS_total, glmmFS_total, glmmTFS_total)## df AIC
## glmmT_total 15 9994.907
## glmmF_total 15 20357.009
## glmmS_total 15 22496.812
## glmmTF_total 16 9849.845
## glmmTS_total 16 9946.192
## glmmFS_total 16 20359.009
## glmmTFS_total 17 9851.845#Now we need to compare if the models are statistically different or not
anova(glmmT_total, glmmTF_total) #significant differences, this two models differ statistically## Data: week_kuds
## Models:
## glmmT_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total: MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmT_total 15 9994.9 10117.2 -4982.5 9964.9
## glmmTF_total 16 9849.8 9980.3 -4908.9 9817.8 147.06 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(glmmT_total, glmmTFS_total) #significant differences, this two models differ statistically## Data: week_kuds
## Models:
## glmmT_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total: MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total: MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmT_total 15 9994.9 10117.2 -4982.5 9964.9
## glmmTFS_total 17 9851.8 9990.4 -4908.9 9817.8 147.06 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(glmmTF_total, glmmTFS_total) #non significant differences, this two models do not differ statistically## Data: week_kuds
## Models:
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total: MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total 16 9849.8 9980.3 -4908.9 9817.8
## glmmTFS_total 17 9851.8 9990.4 -4908.9 9817.8 0 1 1anova(glmmTF_total, glmmTS_total) #non significant differences, this two models do not differ statistically## Data: week_kuds
## Models:
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total: MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total 16 9849.8 9980.3 -4908.9 9817.8
## glmmTS_total 16 9946.2 10076.6 -4957.1 9914.2 0 0 1anova(glmmTS_total, glmmTFS_total) #significant differences, this two models differ statistically## Data: week_kuds
## Models:
## glmmTS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total: MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total: MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTS_total 16 9946.2 10076.6 -4957.1 9914.2
## glmmTFS_total 17 9851.8 9990.4 -4908.9 9817.8 96.347 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#choose the simplest model with the lower AIC, that is statistically different from the others, which means the model with Transmitter and File as random effect (glmmTF_total)Having chosen the best variable to include in the model as random effect, we now must do a backward stepwise selection, to investigate which are the best predictor variables to explain the response behaviour.
First we evaluated the significance of each biological trait in explaining the KUDs alone.
#testing all variables separately using GLMM and File and Transmitter as random effects
summary(glmmTMB(KUD95 ~ LengthStd + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9889.6 9930.4 -4939.8 9879.6 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0710 0.2665
## File (Intercept) 0.1181 0.3436
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.06531 0.07559 -0.864 0.38758
## LengthStd 0.33829 0.10725 3.154 0.00161 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD50 ~ LengthStd + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76334.6 -76293.9 38172.3 -76344.6 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05931 0.2435
## File (Intercept) 0.08807 0.2968
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.64527 0.06723 -24.474 <2e-16 ***
## LengthStd 0.31814 0.09750 3.263 0.0011 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD95 ~ BodyMassStd + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9894.5 9935.2 -4942.2 9884.5 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07131 0.2670
## File (Intercept) 0.11619 0.3409
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06705 0.05478 1.224 0.221
## BodyMassStd 0.19236 0.08520 2.258 0.024 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD50 ~ BodyMassStd + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76330.1 -76289.4 38170.1 -76340.1 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05953 0.2440
## File (Intercept) 0.08635 0.2939
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.52306 0.04768 -31.95 <2e-16 ***
## BodyMassStd 0.19124 0.07706 2.48 0.0131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD95 ~ Longevity + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD95 ~ Longevity + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9897.6 9938.4 -4943.8 9887.6 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07179 0.2679
## File (Intercept) 0.10514 0.3243
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.214929 0.090606 2.372 0.0177 *
## Longevity -0.004947 0.003465 -1.428 0.1533
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD50 ~ Longevity + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD50 ~ Longevity + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76325.9 -76285.1 38167.9 -76335.9 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05996 0.2449
## File (Intercept) 0.07747 0.2783
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.391272 0.078197 -17.79 <2e-16 ***
## Longevity -0.004212 0.002988 -1.41 0.159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD95 ~ Vulnerability + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD95 ~ Vulnerability + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9899.1 9939.9 -4944.6 9889.1 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07177 0.2679
## File (Intercept) 0.10943 0.3308
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.053889 0.236521 -0.228 0.820
## Vulnerability 0.002727 0.003917 0.696 0.486summary(glmmTMB(KUD50 ~ Vulnerability + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD50 ~ Vulnerability + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76324.6 -76283.9 38167.3 -76334.6 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05993 0.2448
## File (Intercept) 0.08076 0.2842
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.647927 0.205059 -8.036 9.26e-16 ***
## Vulnerability 0.002789 0.003396 0.821 0.411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD95 ~ Troph + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD95 ~ Troph + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9896.6 9937.4 -4943.3 9886.6 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07176 0.2679
## File (Intercept) 0.10345 0.3216
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6394 0.4286 -1.492 0.1357
## Troph 0.1952 0.1115 1.752 0.0798 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD50 ~ Troph + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD50 ~ Troph + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76326.7 -76286.0 38168.4 -76336.7 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05993 0.2448
## File (Intercept) 0.07693 0.2774
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.10707 0.37184 -5.667 1.46e-08 ***
## Troph 0.16317 0.09669 1.688 0.0915 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD95 ~ Habitat + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD95 ~ Habitat + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9877.1 9926.0 -4932.6 9865.1 25606
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07151 0.2674
## File (Intercept) 0.06482 0.2546
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.18757 0.07075 2.651 0.00802 **
## Habitatdemersal -0.24497 0.08850 -2.768 0.00564 **
## Habitatpelagic-neritic 0.42181 0.13420 3.143 0.00167 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD50 ~ Habitat + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD50 ~ Habitat + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76343.7 -76294.8 38177.8 -76355.7 25606
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0597 0.2443
## File (Intercept) 0.0514 0.2267
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.40232 0.06315 -22.207 < 2e-16 ***
## Habitatdemersal -0.22015 0.07905 -2.785 0.00535 **
## Habitatpelagic-neritic 0.32112 0.11996 2.677 0.00743 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD95 ~ Migration + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD95 ~ Migration + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9889.9 9930.6 -4939.9 9879.9 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07168 0.2677
## File (Intercept) 0.08889 0.2981
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.008029 0.054731 0.147 0.88337
## Migrationoceanodromous 0.327171 0.100541 3.254 0.00114 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD50 ~ Migration + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD50 ~ Migration + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76332.6 -76291.8 38171.3 -76342.6 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05985 0.2446
## File (Intercept) 0.06766 0.2601
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.56387 0.04798 -32.60 < 2e-16 ***
## Migrationoceanodromous 0.26732 0.08815 3.03 0.00243 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(glmmTMB(KUD95 ~ ComImport + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD95 ~ ComImport + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9899.3 9948.2 -4943.7 9887.3 25606
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07185 0.2680
## File (Intercept) 0.10416 0.3227
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10153 0.06787 1.496 0.135
## ComImportmedium 0.07705 0.10793 0.714 0.475
## ComImportminor -0.17018 0.15358 -1.108 0.268summary(glmmTMB(KUD50 ~ ComImport + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Family: Gamma ( log )
## Formula: KUD50 ~ ComImport + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76325.1 -76276.2 38168.5 -76337.1 25606
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06004 0.2450
## File (Intercept) 0.07540 0.2746
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.47727 0.05798 -25.478 <2e-16 ***
## ComImportmedium 0.05264 0.09244 0.569 0.569
## ComImportminor -0.19610 0.13200 -1.486 0.137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1We found that length, body mass, habitat and migration could alone explain the variability of KUDs. However, we investigated if the models could reach a better fit by including more variables. We did that be performing Backward Elimination.
Final1 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final1)## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +
## Troph + Habitat + Migration + ComImport + ReceiverDensity +
## MonitArea_km2 + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9849.8 9980.3 -4908.9 9817.8 25596
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07066 0.2658
## File (Intercept) 0.02636 0.1624
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1169881 0.3683951 -0.318 0.75082
## LengthStd 0.3830226 0.1540939 2.486 0.01293 *
## BodyMassStd -0.0720452 0.1157550 -0.622 0.53368
## Longevity -0.0030766 0.0025713 -1.197 0.23148
## Vulnerability -0.0026522 0.0036965 -0.717 0.47307
## Troph 0.0265126 0.0994712 0.267 0.78983
## Habitatdemersal -0.1554077 0.0968950 -1.604 0.10874
## Habitatpelagic-neritic 0.3824890 0.1406234 2.720 0.00653 **
## Migrationoceanodromous 0.0900362 0.1091991 0.825 0.40965
## ComImportmedium -0.1673507 0.0669438 -2.500 0.01242 *
## ComImportminor -0.1368534 0.1144496 -1.196 0.23179
## ReceiverDensity 0.0003723 0.0009237 0.403 0.68689
## MonitArea_km2 0.0304967 0.0057906 5.267 1.39e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final2 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final2)## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +
## Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 +
## (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9847.9 9970.2 -4909.0 9817.9 25597
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07064 0.2658
## File (Intercept) 0.02644 0.1626
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0374624 0.2158403 -0.174 0.862207
## LengthStd 0.3808319 0.1539066 2.474 0.013345 *
## BodyMassStd -0.0722796 0.1157847 -0.624 0.532458
## Longevity -0.0028456 0.0024234 -1.174 0.240313
## Vulnerability -0.0023057 0.0034650 -0.665 0.505775
## Habitatdemersal -0.1644190 0.0909344 -1.808 0.070590 .
## Habitatpelagic-neritic 0.4036482 0.1163462 3.469 0.000522 ***
## Migrationoceanodromous 0.0805260 0.1033422 0.779 0.435852
## ComImportmedium -0.1661638 0.0668680 -2.485 0.012957 *
## ComImportminor -0.1303910 0.1119618 -1.165 0.244180
## ReceiverDensity 0.0004378 0.0008916 0.491 0.623429
## MonitArea_km2 0.0305481 0.0057930 5.273 1.34e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final3 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Habitat + Migration + ComImport + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final3)## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +
## Habitat + Migration + ComImport + MonitArea_km2 + (1 | Transmitter) +
## (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9846.2 9960.3 -4909.1 9818.2 25598
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07066 0.2658
## File (Intercept) 0.02653 0.1629
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.017081 0.211953 -0.081 0.935769
## LengthStd 0.388553 0.153163 2.537 0.011185 *
## BodyMassStd -0.078620 0.115136 -0.683 0.494706
## Longevity -0.002743 0.002417 -1.134 0.256585
## Vulnerability -0.002442 0.003458 -0.706 0.480049
## Habitatdemersal -0.164996 0.091033 -1.812 0.069910 .
## Habitatpelagic-neritic 0.393935 0.114744 3.433 0.000597 ***
## Migrationoceanodromous 0.084982 0.103076 0.824 0.409675
## ComImportmedium -0.160946 0.066102 -2.435 0.014899 *
## ComImportminor -0.125807 0.111709 -1.126 0.260083
## MonitArea_km2 0.029120 0.005016 5.805 6.44e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final4 <- glmmTMB(KUD95 ~ LengthStd + Longevity + Vulnerability + Habitat + Migration + ComImport + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final4)## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + Longevity + Vulnerability + Habitat + Migration +
## ComImport + MonitArea_km2 + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9844.6 9950.6 -4909.3 9818.6 25599
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07063 0.2658
## File (Intercept) 0.02715 0.1648
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.019529 0.213713 -0.091 0.927191
## LengthStd 0.312198 0.104371 2.991 0.002778 **
## Longevity -0.002800 0.002438 -1.149 0.250638
## Vulnerability -0.002136 0.003458 -0.618 0.536739
## Habitatdemersal -0.152228 0.089843 -1.694 0.090195 .
## Habitatpelagic-neritic 0.398410 0.115612 3.446 0.000569 ***
## Migrationoceanodromous 0.088576 0.103919 0.852 0.394015
## ComImportmedium -0.166777 0.066131 -2.522 0.011672 *
## ComImportminor -0.136605 0.111491 -1.225 0.220480
## MonitArea_km2 0.029437 0.005037 5.845 5.07e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final5 <- glmmTMB(KUD95 ~ LengthStd + Longevity + Habitat + Migration + ComImport + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final5)## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + Longevity + Habitat + Migration + ComImport +
## MonitArea_km2 + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9843.0 9940.8 -4909.5 9819.0 25600
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07057 0.2657
## File (Intercept) 0.02764 0.1662
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.131811 0.113573 -1.161 0.245813
## LengthStd 0.304964 0.103867 2.936 0.003324 **
## Longevity -0.003603 0.002074 -1.737 0.082365 .
## Habitatdemersal -0.150962 0.090431 -1.669 0.095045 .
## Habitatpelagic-neritic 0.382986 0.113554 3.373 0.000744 ***
## Migrationoceanodromous 0.075081 0.102365 0.733 0.463276
## ComImportmedium -0.161914 0.066111 -2.449 0.014321 *
## ComImportminor -0.100962 0.095989 -1.052 0.292889
## MonitArea_km2 0.030287 0.004875 6.212 5.22e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final6 <- glmmTMB(KUD95 ~ LengthStd + Longevity + Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final6)## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + Longevity + Habitat + ComImport + MonitArea_km2 +
## (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9841.5 9931.2 -4909.8 9819.5 25601
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07058 0.2657
## File (Intercept) 0.02815 0.1678
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.085335 0.094629 -0.902 0.3672
## LengthStd 0.300726 0.103895 2.895 0.0038 **
## Longevity -0.003393 0.002070 -1.639 0.1012
## Habitatdemersal -0.194645 0.068852 -2.827 0.0047 **
## Habitatpelagic-neritic 0.418268 0.103882 4.026 5.66e-05 ***
## ComImportmedium -0.161414 0.066588 -2.424 0.0153 *
## ComImportminor -0.103231 0.096567 -1.069 0.2851
## MonitArea_km2 0.029524 0.004799 6.152 7.64e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final7 <- glmmTMB(KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final7)## Family: Gamma ( log )
## Formula:
## KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 |
## Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9842.1 9923.6 -4911.1 9822.1 25602
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07057 0.2656
## File (Intercept) 0.03045 0.1745
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.17916 0.07802 -2.296 0.02165 *
## LengthStd 0.31695 0.10366 3.058 0.00223 **
## Habitatdemersal -0.19042 0.07077 -2.691 0.00713 **
## Habitatpelagic-neritic 0.46219 0.10371 4.456 8.33e-06 ***
## ComImportmedium -0.15810 0.06865 -2.303 0.02129 *
## ComImportminor -0.06784 0.09678 -0.701 0.48328
## MonitArea_km2 0.02924 0.00494 5.919 3.24e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(Final1, Final7) #as there is no evidence that this two models are statistically different from each other, we must choose the simplest one, the one with less variables (Final7)## Data: week_kuds
## Models:
## Final7: KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7: Transmitter) + (1 | File), zi=~0, disp=~1
## Final1: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## Final1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## Final1: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## Final7 10 9842.1 9923.6 -4911.1 9822.1
## Final1 16 9849.8 9980.3 -4908.9 9817.8 4.2809 6 0.6387ranef(Final7) #deviation from intercept for each Transmitter and File group## $Transmitter
## (Intercept)
## 1 6.405474e-02
## 10 1.746010e-01
## 11 -1.254965e-02
## 12 -2.482687e-01
## 123 1.910382e-01
## 1248320 -3.559846e-02
## 1248321 -2.747798e-02
## 1248322 -3.162221e-02
## 1248323 -4.358297e-03
## 1248324 -4.963134e-02
## 1248325 -5.869448e-03
## 1248326 1.958943e-03
## 1248327 -2.025065e-02
## 1248328 8.659911e-03
## 1248329 -1.517270e-02
## 1248330 2.468471e-02
## 1248331 1.090403e-02
## 1248332 1.708748e-02
## 1248333 3.851312e-02
## 1248334 -4.478196e-02
## 1248335 -2.875393e-03
## 13 -2.491648e-02
## 131 2.272587e-01
## 14 7.408805e-02
## 15 2.184168e-01
## 1511 -1.695419e-01
## 1512 -2.589407e-01
## 1513 -6.367499e-02
## 1516 1.135622e-01
## 1517 3.659809e-01
## 1519 -9.374356e-02
## 1521 4.344713e-02
## 1522 1.711739e-01
## 1523 7.005631e-02
## 1524 6.432722e-02
## 1527 1.196300e-01
## 1528 -2.956752e-01
## 1530 1.965549e-03
## 1531 5.914134e-02
## 1532 1.948517e-01
## 1533 1.050734e-02
## 1534 -1.154551e-01
## 1536 4.038420e-02
## 1537 -1.842975e-01
## 1538 -2.591418e-01
## 1539 -2.303374e-01
## 1540 2.363888e-01
## 1541 -1.048464e-01
## 1542 -6.234554e-02
## 1543 1.648346e-01
## 1544 2.359796e-01
## 1545 -1.612920e-01
## 1546 -4.015817e-02
## 1547 -1.305814e-01
## 1549 -8.481612e-02
## 1551 1.056908e-01
## 1552 2.536657e-02
## 1553 -2.042810e-01
## 1554 -2.334583e-02
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## 1566 -1.097827e-01
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## 18 3.301210e-01
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## Linguado 19 -6.788542e-03
## Linguado 20 -5.703919e-03
## Linguado 21 2.798826e-01
## Linguado 22 2.259836e-01
## Sargo 02 2.203801e-01
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## Sargo 06 -9.117984e-02
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## Sargo 17 4.370562e-02
## Sargo 18 5.317801e-01
## Sargo 19 6.196047e-01
## Sargo 20 -1.289899e-01
##
## $File
## (Intercept)
## Dactylopterus_volitans -0.030764396
## Dentex_dentex1 0.124035061
## Dentex_dentex2 -0.071148256
## Dicentrarchus_labrax1 -0.181767093
## Dicentrarchus_labrax2 0.286189282
## Diplodus_cervinus 0.005946913
## Diplodus_sargus1 0.097833070
## Diplodus_sargus2 0.124010843
## Diplodus_sargus3 -0.013734195
## Diplodus_sargus4 0.012312417
## Diplodus_sargus5 0.101662256
## Diplodus_sargus6 -0.201851389
## Diplodus_vulgaris1 0.274578098
## Diplodus_vulgaris2 0.059321075
## Epinephelus_marginatus1 -0.188445545
## Epinephelus_marginatus2 -0.058604100
## Epinephelus_marginatus3 -0.039599518
## Epinephelus_marginatus4 -0.257476544
## Gadus_morhua1 0.079959135
## Gadus_morhua2 -0.135247927
## Gadus_morhua3 -0.032119264
## Labrus_bergylta 0.130005917
## Lichia_amia 0.182192111
## Lithognathus_mormyrus -0.027538827
## Pagellus_erythrinus 0.134187748
## Pagrus_pagrus1 -0.218414822
## Pagrus_pagrus2 0.095225953
## Pomatomus_saltatrix 0.187394788
## Pseudocaranx_dentex 0.105967770
## Sciaena_umbra1 -0.013882449
## Sciaena_umbra2 0.001112470
## Scorpaena_porcus 0.029053652
## Scorpaena_scrofa1 0.072281810
## Scorpaena_scrofa2 -0.253232465
## Seriola_dumerili 0.171683353
## Seriola_rivoliana -0.264640988
## Serranus_atricauda -0.170683790
## Serranus_cabrilla 0.151877204
## Serranus_scriba -0.096518097
## Solea_senegalensis 0.149053078
## Sparisoma_cretense -0.198398640
## Sparus_aurata1 -0.014263407
## Sparus_aurata2 0.287005810
## Sphyraena_viridensis1 -0.319299094
## Sphyraena_viridensis2 0.036032842
## Spondyliosoma_cantharus -0.190778571
## Umbrina_cirrosa -0.054130977
## Xyrichtys_novacula 0.002109028confint(Final7)## 2.5 % 97.5 % Estimate
## (Intercept) -0.33207281 -0.02624889 -0.17916085
## LengthStd 0.11378680 0.52011581 0.31695131
## Habitatdemersal -0.32913214 -0.05171090 -0.19042152
## Habitatpelagic-neritic 0.25891832 0.66547022 0.46219427
## ComImportmedium -0.29264888 -0.02354206 -0.15809547
## ComImportminor -0.25752598 0.12183717 -0.06784440
## MonitArea_km2 0.01955517 0.03891805 0.02923661
## Std.Dev.(Intercept)|Transmitter 0.25110509 0.28102709 0.26564513
## Std.Dev.(Intercept)|File 0.13545765 0.22476657 0.17448883#evaluate if the final model is better than the ones including only one biological trait
anova(Final7, glmmTMB(KUD95 ~ LengthStd + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Data: week_kuds
## Models:
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")): KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Final7: KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7: Transmitter) + (1 | File), zi=~0, disp=~1
## Df
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 5
## Final7 10
## AIC
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9889.6
## Final7 9842.1
## BIC
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9930.4
## Final7 9923.6
## logLik
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -4939.8
## Final7 -4911.1
## deviance
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9879.6
## Final7 9822.1
## Chisq
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 57.507
## Chi Df
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 5
## Pr(>Chisq)
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 3.975e-11
##
## glmmTMB(KUD95 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(Final7, glmmTMB(KUD95 ~ BodyMassStd + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Data: week_kuds
## Models:
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")): KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Final7: KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7: Transmitter) + (1 | File), zi=~0, disp=~1
## Df
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 5
## Final7 10
## AIC
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9894.5
## Final7 9842.1
## BIC
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9935.2
## Final7 9923.6
## logLik
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -4942.2
## Final7 -4911.1
## deviance
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9884.5
## Final7 9822.1
## Chisq
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 62.366
## Chi Df
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 5
## Pr(>Chisq)
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 3.938e-12
##
## glmmTMB(KUD95 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(Final7, glmmTMB(KUD95 ~ Habitat + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Data: week_kuds
## Models:
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")): KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Final7: KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7: Transmitter) + (1 | File), zi=~0, disp=~1
## Df
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 6
## Final7 10
## AIC
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9877.1
## Final7 9842.1
## BIC
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9926.0
## Final7 9923.6
## logLik
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -4932.6
## Final7 -4911.1
## deviance
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9865.1
## Final7 9822.1
## Chisq
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 43.013
## Chi Df
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 4
## Pr(>Chisq)
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 1.028e-08
##
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(Final7, glmmTMB(KUD95 ~ Migration + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Data: week_kuds
## Models:
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")): KUD95 ~ Migration + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Final7: KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7: Transmitter) + (1 | File), zi=~0, disp=~1
## Df
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 5
## Final7 10
## AIC
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9889.9
## Final7 9842.1
## BIC
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9930.6
## Final7 9923.6
## logLik
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -4939.9
## Final7 -4911.1
## deviance
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 9879.9
## Final7 9822.1
## Chisq
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 57.752
## Chi Df
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 5
## Pr(>Chisq)
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 3.538e-11
##
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1We reached the best fitted model (Final7). Now we must analyse the residuals to see how they behave.
#Test residuals for the best model (goodness of fit)
testDispersion(Final7) #plot with normality, dispersion and outliers##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.7239, p-value < 2.2e-16
## alternative hypothesis: two.sidedsimulationOutput <- simulateResiduals(fittedModel = Final7, plot = F) #dispersion test
testDispersion(simulationOutput) #dispersion test
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.7239, p-value < 2.2e-16
## alternative hypothesis: two.sidedplot(simulationOutput) #residual analysis
plotQQunif(simulationOutput) #Q-Q plot (normality checking)
plotResiduals(simulationOutput) #residual vs. predicted (homoscedasticity checking)
testOutliers(simulationOutput) #outliers checking
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: simulationOutput
## outliers at both margin(s) = 278, observations = 25612, p-value =
## 8.223e-07
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
## 0.009621516 0.012199842
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 )
## 0.01085429#Simulations from the model
getObservedResponse(Final7) #response used to fit the model## [1] 1.079 0.839 3.494 0.819 1.162 0.764 0.893 1.385 1.284 1.772
## [11] 1.433 1.812 2.506 1.990 0.762 0.762 0.879 0.990 1.074 1.063
## [21] 1.078 1.020 1.103 0.909 1.131 0.865 0.864 0.874 0.898 0.873
## [31] 1.736 1.943 1.911 1.812 1.923 1.906 1.931 1.969 1.818 1.800
## [41] 1.884 1.790 1.642 1.577 1.780 1.762 1.762 1.803 1.782 1.871
## [51] 1.819 0.929 1.496 1.629 1.738 1.714 1.779 1.851 1.842 1.362
## [61] 1.697 1.587 1.923 1.977 1.733 2.198 2.102 2.254 2.059 1.993
## [71] 1.755 1.862 1.833 1.751 1.797 1.726 1.699 1.685 1.774 1.814
## [81] 1.914 1.724 1.840 1.799 0.856 1.668 1.138 1.003 0.902 0.960
## [91] 0.912 0.840 0.899 0.903 0.913 0.916 1.003 0.931 0.836 0.840
## [101] 0.835 0.795 0.827 0.841 0.829 0.842 0.820 0.818 0.813 1.044
## [111] 1.117 1.126 1.121 1.096 1.077 1.160 1.012 1.020 1.716 0.983
## [121] 0.868 0.858 1.061 1.100 0.883 0.885 0.850 1.673 1.012 1.060
## [131] 0.874 0.862 0.998 1.788 1.553 1.132 1.659 1.383 0.919 0.835
## [141] 0.924 0.956 0.955 0.839 1.104 1.002 1.093 1.125 1.072 1.019
## [151] 1.011 1.138 1.078 1.072 1.099 1.083 0.962 1.107 1.078 1.113
## [161] 1.116 1.278 0.937 1.136 1.134 1.130 1.103 1.118 1.119 1.102
## [171] 1.121 0.933 0.855 1.259 1.454 1.069 0.912 0.786 0.997 1.621
## [181] 1.606 1.702 1.469 1.001 1.512 1.652 1.264 1.221 1.552 1.881
## [191] 0.928 1.161 0.967 1.043 1.138 0.951 1.273 0.818 0.893 0.816
## [201] 0.918 1.083 1.064 0.918 0.889 1.055 0.961 1.123 0.951 1.128
## [211] 1.111 1.037 1.106 1.119 1.101 0.949 1.427 1.653 1.950 1.734
## [221] 1.041 0.937 1.963 2.062 0.924 0.928 1.187 2.061 1.679 1.728
## [231] 1.763 1.623 1.945 2.114 1.274 1.029 1.531 1.430 1.611 0.938
## [241] 0.958 0.919 0.856 0.910 0.851 0.805 0.803 0.838 0.920 0.799
## [251] 0.781 0.782 0.936 0.878 1.037 1.020 1.141 1.177 1.373 1.298
## [261] 1.230 1.159 1.210 1.188 1.225 1.349 1.124 1.059 0.979 1.912
## [271] 1.478 1.047 0.982 1.227 1.516 1.508 1.334 1.612 1.280 1.173
## [281] 1.508 1.366 0.966 1.781 1.802 1.390 1.100 0.911 0.923 0.996
## [291] 0.818 1.075 0.946 0.980 0.959 1.040 1.117 1.061 1.005 1.113
## [301] 1.134 1.094 1.076 1.200 1.083 1.057 1.119 1.030 1.037 0.992
## [311] 1.013 1.051 1.055 1.023 0.938 0.959 1.049 1.120 1.107 1.138
## [321] 1.166 1.222 1.304 0.794 0.879 0.912 1.146 1.060 1.103 1.457
## [331] 1.487 1.597 1.793 1.560 1.959 1.751 1.593 1.651 1.028 1.648
## [341] 1.137 1.148 1.386 1.306 1.435 1.373 1.538 1.389 1.529 1.386
## [351] 1.502 1.803 1.854 1.887 1.829 2.110 2.254 1.646 1.921 2.071
## [361] 2.030 1.990 1.820 1.599 2.043 1.736 1.996 2.074 2.948 2.175
## [371] 1.843 2.186 1.510 1.825 1.847 1.512 1.349 1.623 0.984 0.933
## [381] 1.253 1.123 1.405 0.948 1.535 0.792 0.790 0.775 0.807 0.771
## [391] 0.782 0.776 0.769 0.771 0.791 1.307 1.012 0.868 1.220 1.172
## [401] 0.864 0.845 0.950 1.023 0.969 0.807 0.879 1.169 1.054 0.916
## [411] 1.289 1.037 1.305 1.052 1.356 1.685 1.732 1.323 1.282 1.253
## [421] 1.806 1.748 1.215 1.174 1.096 1.403 1.235 1.584 0.897 0.864
## [431] 0.912 0.829 0.848 0.824 0.803 0.798 0.807 0.970 0.940 0.817
## [441] 0.824 0.822 0.797 0.801 0.808 0.796 0.870 0.884 0.900 0.901
## [451] 0.816 0.904 0.937 0.940 1.833 1.672 0.977 0.979 1.051 1.554
## [461] 0.793 0.831 0.914 0.871 0.847 0.939 0.953 0.899 0.860 0.884
## [471] 0.908 0.915 0.867 0.836 1.064 0.850 0.863 0.810 0.980 0.870
## [481] 1.015 0.923 1.050 1.082 1.042 1.041 1.112 1.040 1.022 1.012
## [491] 0.961 1.032 0.898 0.785 0.829 1.058 1.049 0.905 0.788 0.789
## [501] 1.784 1.293 1.504 1.516 0.980 1.488 1.827 1.453 1.136 1.815
## [511] 1.667 1.423 1.564 0.882 1.024 1.042 1.078 0.792 0.812 1.198
## [521] 1.227 1.228 1.578 1.262 1.117 1.325 1.080 1.150 1.578 1.570
## [531] 1.509 1.192 1.720 1.520 1.606 1.407 1.252 1.264 1.392 1.353
## [541] 0.821 1.172 1.328 1.354 1.917 1.520 1.332 1.505 1.495 1.195
## [551] 1.459 1.257 1.298 1.074 1.277 1.266 1.222 1.120 1.471 1.298
## [561] 1.300 1.172 1.374 1.229 1.178 1.183 1.311 1.176 1.209 1.148
## [571] 1.181 1.648 0.820 0.800 0.801 0.788 0.797 0.800 0.808 0.809
## [581] 0.786 0.793 0.794 0.794 0.799 0.808 0.804 0.906 0.983 0.939
## [591] 0.895 0.893 0.819 0.795 0.834 0.791 0.795 0.785 0.800 0.893
## [601] 0.782 0.773 0.800 0.971 0.772 0.770 0.776 0.782 0.796 0.784
## [611] 0.861 0.767 0.823 0.771 0.783 0.800 0.802 0.924 0.803 0.799
## [621] 0.821 0.791 0.810 0.805 0.793 1.081 1.066 1.094 1.102 1.066
## [631] 1.132 1.074 1.092 1.121 1.156 1.111 1.114 1.083 1.143 1.102
## [641] 1.159 1.124 1.169 1.293 1.341 1.360 1.340 1.164 1.033 1.166
## [651] 1.230 1.358 1.294 1.302 1.567 1.338 1.318 1.270 1.243 1.183
## [661] 1.476 1.362 1.350 1.227 1.201 1.229 1.226 1.149 1.142 1.378
## [671] 0.842 1.247 1.120 1.142 1.088 1.089 0.825 0.794 0.827 0.794
## [681] 0.834 0.890 1.146 0.807 0.790 0.798 0.810 0.801 0.915 0.794
## [691] 0.793 0.826 0.823 0.799 0.849 1.262 1.639 0.784 1.391 1.830
## [701] 1.669 1.432 1.491 0.802 0.783 0.879 0.829 0.802 0.789 1.356
## [711] 1.359 1.358 1.275 1.311 1.316 1.341 1.710 1.299 1.196 1.537
## [721] 1.658 1.686 1.190 1.413 1.537 1.576 1.309 1.142 1.431 1.566
## [731] 1.650 1.246 0.983 1.213 1.288 1.420 1.486 1.516 1.163 1.411
## [741] 1.341 1.353 1.226 1.527 1.327 1.322 1.541 1.491 1.442 1.525
## [751] 1.271 1.068 1.128 1.629 1.797 1.642 1.285 1.459 1.786 1.494
## [761] 1.735 1.736 1.705 1.679 1.314 1.364 1.604 1.620 1.654 1.694
## [771] 1.752 1.825 1.719 1.522 1.848 1.710 1.457 1.441 1.630 1.880
## [781] 1.862 1.816 0.880 1.535 1.834 1.413 1.054 1.721 0.982 1.405
## [791] 0.824 0.988 1.009 1.137 1.448 1.327 1.469 1.413 1.212 1.428
## [801] 1.462 1.422 1.252 1.300 1.519 1.468 1.070 0.948 0.926 1.033
## [811] 1.079 0.955 0.840 0.843 0.875 0.796 1.380 0.939 0.766 0.764
## [821] 1.330 1.037 1.384 1.940 2.013 1.703 1.880 2.046 2.011 1.764
## [831] 1.500 1.890 1.996 1.501 1.546 2.650 1.915 1.464 1.350 1.783
## [841] 1.422 1.911 1.807 1.402 1.183 1.336 1.625 1.705 1.199 1.687
## [851] 2.112 1.626 1.959 1.773 1.752 2.016 1.840 1.673 1.341 1.148
## [861] 1.394 1.083 0.857 0.858 2.583 1.746 1.788 1.391 1.767 1.625
## [871] 1.467 1.423 0.877 1.167 0.761 0.762 0.762 0.762 0.762 1.015
## [881] 1.123 1.147 1.307 1.277 1.235 1.200 1.286 1.199 1.448 1.339
## [891] 1.381 1.672 1.310 1.180 1.548 1.501 1.462 1.208 1.507 1.414
## [901] 1.419 1.495 1.280 1.463 0.762 0.762 0.762 0.761 0.761 0.765
## [911] 0.763 0.761 0.763 0.766 0.761 0.765 0.762 0.761 0.761 0.761
## [921] 0.762 0.761 0.761 0.761 0.792 0.761 2.018 1.115 0.847 0.965
## [931] 1.270 1.032 1.309 1.631 1.245 1.355 1.385 1.173 1.299 0.802
## [941] 0.769 1.195 0.936 0.764 0.771 0.762 0.764 0.762 0.761 0.761
## [951] 0.762 0.762 0.761 0.761 0.766 0.929 0.929 1.006 1.019 0.950
## [961] 1.620 1.587 1.661 1.777 1.652 1.582 0.867 1.094 1.653 2.291
## [971] 2.184 2.684 2.598 1.782 0.776 1.723 0.869 0.821 1.863 1.561
## [981] 1.742 1.348 1.415 1.623 1.732 1.820 1.722 1.850 2.007 1.863
## [991] 1.962 1.820 1.859 1.988 2.412 0.761 1.761 1.655 1.063 1.189
## [1001] 0.949 1.010 3.857 1.045 1.243 1.267 1.283 1.063 1.320 1.008
## [1011] 1.348 1.335 1.352 1.281 1.295 1.263 1.209 1.264 1.137 1.028
## [1021] 0.897 1.046 1.048 1.161 1.068 1.335 1.326 1.312 1.350 1.240
## [1031] 1.696 2.186 2.458 0.761 2.594 1.289 1.153 1.233 1.358 1.258
## [1041] 1.334 1.336 1.351 1.321 1.252 1.169 0.766 0.766 0.761 0.766
## [1051] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.762 4.133 3.133
## [1061] 0.763 0.761 0.761 0.762 0.762 0.762 0.761 0.762 0.765 0.761
## [1071] 0.766 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [1081] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1091] 0.761 0.763 0.761 0.763 0.761 0.762 0.761 1.567 1.713 1.394
## [1101] 1.585 0.762 1.573 1.312 1.335 0.898 1.456 0.806 0.826 0.839
## [1111] 0.948 0.952 1.157 1.193 1.166 1.575 2.087 1.509 2.145 1.829
## [1121] 2.255 1.498 2.222 1.760 2.122 2.226 2.227 2.248 2.205 2.066
## [1131] 2.026 1.794 1.717 1.881 1.993 2.109 8.410 7.846 8.805 4.991
## [1141] 3.834 6.338 8.343 6.595 7.410 7.560 6.803 7.530 5.109 5.559
## [1151] 5.943 1.492 1.689 1.608 1.796 0.946 1.024 1.535 0.901 0.846
## [1161] 1.620 1.297 1.415 1.588 1.749 1.275 1.054 1.532 1.882 1.158
## [1171] 1.896 1.722 1.723 2.081 1.995 2.254 1.996 2.209 1.550 2.101
## [1181] 1.674 1.667 1.710 2.215 1.516 2.192 2.050 1.932 2.043 2.179
## [1191] 1.910 2.239 1.964 2.132 2.088 1.994 1.780 1.617 1.894 1.793
## [1201] 1.687 1.499 2.307 1.906 1.819 1.806 1.459 1.925 1.010 1.700
## [1211] 1.040 1.559 1.155 1.644 1.397 1.657 1.791 7.752 7.273 5.443
## [1221] 2.245 2.754 1.496 2.823 3.655 7.074 7.297 2.410 1.601 1.642
## [1231] 2.241 1.344 0.943 1.328 1.328 1.048 1.286 1.209 1.525 1.304
## [1241] 1.098 1.445 1.052 1.681 1.442 1.669 1.765 1.081 1.165 1.429
## [1251] 1.352 1.845 1.781 1.587 1.798 2.282 2.091 2.135 1.883 1.740
## [1261] 1.844 1.747 1.659 1.516 1.819 1.826 1.748 1.761 1.779 1.619
## [1271] 1.959 2.447 1.864 2.127 2.095 1.472 1.705 2.113 1.353 1.848
## [1281] 1.058 1.857 1.120 1.042 1.304 1.038 1.756 1.016 1.517 1.041
## [1291] 1.268 1.162 1.451 1.703 1.567 1.456 1.230 1.071 1.481 1.076
## [1301] 1.601 1.389 2.114 2.345 2.211 2.835 3.964 4.869 2.149 2.740
## [1311] 1.866 1.629 2.017 1.116 0.868 1.265 1.006 1.415 1.031 1.109
## [1321] 1.083 1.302 1.334 0.914 0.978 1.148 1.365 1.361 1.522 1.421
## [1331] 1.561 1.034 1.142 1.023 1.368 1.387 1.425 1.926 1.353 1.989
## [1341] 1.929 1.787 1.932 1.717 1.104 1.477 1.329 1.820 1.283 1.489
## [1351] 1.318 1.576 1.553 1.363 2.196 1.534 2.081 1.384 1.312 1.280
## [1361] 1.091 0.828 1.322 1.192 1.372 1.388 1.413 1.291 1.356 1.260
## [1371] 1.375 1.497 1.355 1.257 1.961 1.275 0.944 0.967 1.114 0.822
## [1381] 0.862 1.068 1.708 1.787 1.947 1.828 1.726 1.726 1.640 1.593
## [1391] 1.415 1.454 1.007 0.761 0.761 0.762 0.762 0.761 1.118 0.761
## [1401] 0.762 0.861 1.252 0.761 0.761 0.762 0.761 0.764 0.762 0.761
## [1411] 0.761 0.761 1.162 0.761 0.826 0.761 0.761 0.761 0.761 0.761
## [1421] 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.761 0.761 0.877
## [1431] 0.761 0.762 0.849 1.333 0.761 0.761 0.766 0.762 0.761 0.764
## [1441] 0.762 0.761 0.764 1.285 0.761 0.761 1.325 1.009 0.761 0.761
## [1451] 0.968 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.233 1.276
## [1461] 1.411 0.764 0.761 0.762 0.761 0.761 1.088 0.761 0.761 0.761
## [1471] 0.761 0.761 0.761 0.761 1.528 0.764 0.761 1.260 0.762 0.761
## [1481] 0.761 1.044 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1491] 0.761 0.761 1.375 1.527 2.818 1.516 1.310 1.263 0.761 1.516
## [1501] 0.761 1.517 1.517 2.257 2.018 0.761 2.932 3.674 2.695 1.452
## [1511] 3.017 1.376 1.517 2.218 1.516 1.526 0.761 1.524 0.762 0.762
## [1521] 0.761 0.761 2.412 1.161 3.188 1.714 3.432 1.593 3.214 3.654
## [1531] 0.761 1.736 1.173 0.761 0.761 0.761 0.761 1.097 0.761 0.761
## [1541] 0.761 1.368 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1551] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [1561] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.762
## [1571] 0.761 0.762 0.761 0.761 0.761 0.762 1.513 1.194 1.468 0.762
## [1581] 0.761 0.761 0.761 0.761 0.761 0.761 1.245 1.516 0.761 0.761
## [1591] 0.766 0.762 0.761 0.901 0.761 0.761 0.761 0.762 0.761 1.526
## [1601] 0.889 1.431 0.762 0.761 0.761 0.761 0.761 0.761 0.955 1.345
## [1611] 1.516 0.761 0.761 0.766 0.762 0.761 0.895 0.761 0.761 0.761
## [1621] 0.761 1.414 1.554 1.725 1.558 1.086 1.195 0.913 1.082 0.762
## [1631] 0.761 0.762 0.761 0.764 0.761 0.761 0.984 1.414 0.926 0.945
## [1641] 1.113 1.126 1.047 1.345 1.488 1.717 1.516 1.104 1.183 1.065
## [1651] 1.007 0.890 0.904 0.764 0.762 0.761 0.761 1.023 0.761 0.761
## [1661] 0.955 1.333 1.140 0.991 1.042 1.104 0.995 0.761 0.761 0.761
## [1671] 0.761 1.524 1.809 1.516 1.148 0.766 0.762 0.761 1.091 0.766
## [1681] 0.762 0.761 1.496 1.439 1.296 1.521 1.411 1.632 1.517 2.391
## [1691] 1.276 1.257 1.387 0.761 0.762 2.068 2.110 0.763 3.779 2.213
## [1701] 2.763 1.842 0.766 1.200 1.928 2.254 0.763 3.663 3.694 3.016
## [1711] 2.175 0.766 0.761 0.761 0.761 0.761 0.764 0.761 0.761 0.761
## [1721] 1.287 3.733 1.411 2.219 2.174 2.984 2.957 1.926 3.514 2.182
## [1731] 2.023 3.849 1.464 1.485 3.615 3.768 2.181 1.453 2.203 1.745
## [1741] 1.340 3.541 3.713 3.418 2.157 2.634 2.261 2.330 1.978 0.761
## [1751] 0.761 0.764 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1761] 0.761 0.761 0.761 0.761 0.761 0.764 0.761 0.761 0.761 0.761
## [1771] 0.761 0.761 0.761 1.376 0.763 0.761 0.761 0.761 0.764 0.761
## [1781] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1791] 0.762 0.761 1.311 0.762 1.156 0.761 0.761 0.761 0.761 0.761
## [1801] 0.761 0.761 0.761 0.761 0.761 1.313 2.478 1.670 0.761 2.204
## [1811] 1.198 0.761 1.374 3.226 2.163 1.205 2.707 1.699 0.761 1.554
## [1821] 1.420 1.319 1.009 1.496 0.764 2.753 2.795 1.534 1.398 1.223
## [1831] 1.499 0.761 0.761 0.764 0.761 0.761 0.761 0.761 0.761 0.761
## [1841] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.763 0.761 0.761
## [1851] 0.761 0.764 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [1861] 0.761 0.761 0.761 0.761 0.970 1.171 1.482 0.766 0.762 0.761
## [1871] 1.041 1.723 1.078 0.762 0.761 1.201 1.062 1.032 1.822 1.892
## [1881] 1.728 1.765 1.019 0.903 0.839 1.651 1.841 1.628 1.715 1.411
## [1891] 1.475 2.052 1.378 3.841 1.072 1.325 1.257 1.011 1.306 1.258
## [1901] 0.761 0.761 0.883 1.036 0.761 0.761 0.836 1.083 1.742 1.213
## [1911] 1.743 1.731 0.761 0.761 0.761 0.761 0.761 0.761 1.593 1.217
## [1921] 0.761 0.761 1.482 1.697 1.357 1.497 1.525 1.694 1.636 1.483
## [1931] 0.761 1.517 1.411 1.704 1.729 1.224 1.716 1.476 0.761 1.517
## [1941] 1.342 1.725 1.694 1.668 1.742 1.466 2.171 2.124 0.761 1.501
## [1951] 1.011 0.761 0.761 1.411 1.562 1.387 0.761 0.761 1.513 1.652
## [1961] 1.475 1.103 0.761 0.761 1.453 1.639 1.572 0.927 0.761 1.517
## [1971] 0.761 1.400 1.493 0.761 1.310 1.480 0.761 0.761 1.454 1.370
## [1981] 0.764 0.762 0.761 0.761 1.388 1.442 0.927 1.297 1.494 0.761
## [1991] 0.761 1.445 1.381 0.764 0.762 2.346 1.919 1.771 1.753 1.679
## [2001] 1.620 1.666 1.674 1.723 1.672 1.742 1.753 1.691 1.645 1.695
## [2011] 1.670 0.761 0.761 0.761 1.514 0.761 1.186 0.761 0.761 0.761
## [2021] 0.761 0.761 0.761 1.535 0.761 0.761 0.761 0.761 1.517 0.761
## [2031] 1.139 0.761 0.761 0.761 0.761 0.761 0.761 1.532 0.761 1.496
## [2041] 0.761 0.761 0.761 0.761 0.761 0.760 0.761 0.761 0.761 0.761
## [2051] 0.764 0.762 1.502 0.761 0.761 0.761 0.761 0.761 0.760 0.761
## [2061] 0.761 0.761 0.761 0.764 0.762 1.152 1.737 1.647 2.019 1.461
## [2071] 0.761 1.161 1.739 1.632 1.615 1.400 0.761 0.761 0.760 1.825
## [2081] 0.832 0.760 0.760 0.760 0.761 0.761 0.760 0.761 0.760 1.803
## [2091] 0.762 0.760 0.760 0.760 0.761 0.761 0.760 0.761 0.761 0.761
## [2101] 0.761 0.761 0.761 0.761 0.761 1.326 0.972 0.894 0.893 1.219
## [2111] 0.790 1.423 1.146 0.914 0.907 0.849 0.768 1.473 1.463 1.339
## [2121] 1.477 1.499 1.475 1.524 1.508 1.523 1.447 1.353 1.165 1.469
## [2131] 1.313 1.528 1.412 1.406 1.457 1.354 1.477 1.476 1.457 1.519
## [2141] 1.495 1.523 1.461 1.386 1.213 1.457 1.339 1.526 1.877 1.490
## [2151] 2.716 1.813 0.762 0.761 0.761 4.662 0.783 1.149 0.774 1.073
## [2161] 0.812 1.844 2.007 0.968 0.814 1.411 0.874 1.950 1.488 0.839
## [2171] 2.564 1.493 1.143 0.761 2.128 1.809 1.855 1.208 0.911 1.260
## [2181] 1.691 0.822 1.260 1.256 2.799 0.985 0.762 0.761 4.898 0.870
## [2191] 0.761 1.116 1.427 1.120 2.081 2.024 0.915 0.822 1.383 1.029
## [2201] 1.862 1.338 0.793 2.253 1.220 1.032 0.761 2.233 1.458 1.218
## [2211] 1.133 0.769 1.284 1.510 0.909 1.527 1.386 1.524 1.526 1.296
## [2221] 1.526 1.339 1.457 1.513 0.761 1.363 1.487 1.413 1.535 1.521
## [2231] 1.474 1.527 1.368 1.526 1.524 1.324 1.525 1.358 1.456 1.516
## [2241] 0.761 1.369 1.470 1.453 1.531 1.527 1.474 0.763 5.602 0.906
## [2251] 0.762 1.994 1.996 0.849 1.949 3.564 3.282 0.761 1.803 0.763
## [2261] 0.860 3.306 2.570 2.866 3.495 1.590 3.759 1.740 0.897 3.517
## [2271] 3.326 2.138 2.861 1.548 0.761 3.365 4.136 2.711 0.808 0.761
## [2281] 0.761 0.762 1.819 4.482 2.947 2.276 1.314 3.196 1.902 1.644
## [2291] 4.300 1.381 1.976 2.350 2.811 1.401 1.313 2.580 2.143 2.310
## [2301] 2.124 3.354 1.304 2.123 1.903 2.123 2.701 1.344 0.761 2.731
## [2311] 2.893 0.761 1.584 0.761 5.206 2.251 4.528 3.570 1.529 2.272
## [2321] 2.751 2.619 1.178 4.502 2.775 4.483 5.689 2.514 7.286 1.729
## [2331] 1.455 1.374 2.030 2.073 2.116 1.751 1.455 1.527 0.761 0.802
## [2341] 3.374 1.154 2.435 1.619 3.440 0.762 0.762 4.270 4.847 3.389
## [2351] 2.024 3.062 0.762 2.459 0.764 2.213 0.762 3.317 1.918 4.377
## [2361] 1.743 6.218 1.832 3.935 3.502 4.627 2.368 4.496 4.090 4.301
## [2371] 2.478 0.762 0.767 1.712 2.395 0.871 2.035 2.095 0.761 1.839
## [2381] 1.440 1.066 1.679 0.761 0.761 0.763 2.505 1.345 0.761 0.761
## [2391] 0.762 0.762 0.761 0.761 0.761 0.761 0.906 0.761 0.761 1.237
## [2401] 0.763 1.703 0.817 0.761 0.761 0.761 1.300 0.761 0.761 0.761
## [2411] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [2421] 0.761 0.762 0.761 0.763 0.761 0.761 0.761 0.761 2.816 1.241
## [2431] 4.097 0.764 0.761 2.090 1.627 1.526 1.905 1.850 2.109 2.213
## [2441] 3.372 3.291 3.445 3.413 2.982 1.836 1.906 1.931 2.053 1.857
## [2451] 1.699 1.745 1.981 1.486 1.465 2.216 1.278 1.282 0.762 0.761
## [2461] 2.483 1.034 1.163 1.038 6.556 1.254 1.512 1.343 1.315 1.077
## [2471] 3.120 0.762 0.761 2.911 1.394 6.831 1.241 7.292 0.761 1.931
## [2481] 1.201 1.817 3.749 2.091 2.894 1.424 1.379 0.761 4.314 3.429
## [2491] 1.246 2.991 2.512 3.412 0.762 2.822 6.282 2.099 2.991 1.271
## [2501] 1.982 1.894 1.640 1.848 1.592 1.698 1.824 3.602 1.951 1.676
## [2511] 2.512 1.472 1.926 3.774 1.701 3.666 3.127 2.247 1.661 1.935
## [2521] 2.295 1.880 0.764 1.493 3.747 4.277 3.346 4.987 4.750 5.731
## [2531] 0.763 0.761 1.225 0.761 3.001 2.046 2.365 3.716 1.473 3.190
## [2541] 4.881 1.336 1.393 3.413 1.250 1.578 1.574 1.403 1.519 1.254
## [2551] 1.548 1.846 1.662 0.840 1.560 1.520 1.724 1.217 1.848 1.314
## [2561] 1.191 1.357 1.440 1.611 1.502 1.527 1.501 1.386 1.464 1.541
## [2571] 1.430 1.585 1.359 1.560 1.015 1.559 0.762 1.417 0.802 1.637
## [2581] 0.762 1.271 0.762 1.293 0.762 1.561 1.569 0.762 1.543 0.762
## [2591] 1.613 1.625 1.041 1.663 1.562 1.378 1.523 1.461 1.562 1.489
## [2601] 1.407 1.481 1.451 1.424 1.547 1.693 1.920 1.825 1.957 1.993
## [2611] 1.833 1.724 1.726 1.298 1.491 1.569 0.761 0.762 0.765 0.761
## [2621] 0.761 0.761 0.763 0.761 0.763 0.761 0.761 0.761 0.761 0.761
## [2631] 0.761 0.762 1.360 1.522 1.189 0.761 0.762 0.761 0.761 0.761
## [2641] 0.761 4.983 9.142 2.883 3.143 2.938 3.088 2.740 2.557 1.244
## [2651] 2.253 1.901 1.209 1.587 1.756 1.959 2.011 1.785 1.359 1.813
## [2661] 1.960 1.343 0.761 0.761 0.761 0.766 0.761 0.762 0.761 0.761
## [2671] 0.763 0.761 0.766 0.762 0.761 0.765 0.772 0.761 0.762 0.761
## [2681] 0.856 1.201 0.890 0.787 0.766 0.761 0.778 0.764 1.333 0.761
## [2691] 0.761 0.760 1.090 0.761 0.762 0.883 0.761 0.761 0.969 0.994
## [2701] 0.873 1.077 0.855 0.761 0.762 0.762 0.761 0.761 0.761 0.761
## [2711] 0.761 0.762 0.761 0.761 5.388 6.075 4.080 7.662 9.166 6.857
## [2721] 6.955 6.223 5.543 8.678 10.692 6.300 9.127 5.232 6.299 4.754
## [2731] 14.723 5.417 13.452 3.026 13.020 2.517 7.581 4.883 6.159 4.564
## [2741] 2.584 7.030 3.266 1.220 4.325 3.196 3.482 2.349 2.465 2.893
## [2751] 2.364 2.563 2.204 1.740 1.649 2.349 3.029 2.432 1.281 1.833
## [2761] 1.610 1.877 1.676 1.678 1.719 2.304 1.937 1.629 3.234 2.231
## [2771] 1.840 2.499 3.291 2.769 2.274 3.505 3.080 2.770 3.856 2.325
## [2781] 2.550 1.968 1.409 5.251 1.598 4.289 6.615 2.360 3.621 3.010
## [2791] 2.199 3.563 3.469 3.019 3.413 2.553 3.060 2.545 2.630 3.086
## [2801] 3.400 4.225 3.862 3.509 2.972 2.876 3.812 1.743 3.649 7.300
## [2811] 3.578 6.265 3.600 4.134 1.492 4.235 6.680 2.677 7.472 5.010
## [2821] 4.652 4.589 2.969 3.581 3.344 2.251 2.843 3.097 3.553 5.515
## [2831] 2.485 1.927 4.721 2.309 1.677 1.525 1.686 1.377 1.867 1.409
## [2841] 1.350 1.302 1.741 1.737 2.428 2.190 2.251 3.297 2.576 2.354
## [2851] 2.102 2.673 2.101 2.331 3.647 3.661 2.210 4.367 3.398 4.053
## [2861] 3.764 3.244 3.430 2.425 1.981 3.465 2.070 1.910 3.141 2.196
## [2871] 4.136 1.959 3.651 0.848 2.527 2.989 0.839 1.346 1.337 1.805
## [2881] 1.907 2.204 2.283 1.883 1.468 1.362 1.361 1.453 1.482 1.435
## [2891] 1.387 1.259 1.437 1.368 1.796 1.739 12.571 6.403 3.991 1.286
## [2901] 1.369 1.425 1.470 1.436 1.354 1.397 1.352 1.308 1.301 1.286
## [2911] 1.447 0.761 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761
## [2921] 0.761 0.761 0.761 0.761 0.761 1.003 1.076 0.800 1.081 1.504
## [2931] 1.509 1.395 1.520 0.762 1.458 0.762 1.509 1.486 1.122 0.762
## [2941] 1.433 1.397 1.408 2.756 2.322 1.474 1.519 1.634 1.030 0.977
## [2951] 1.396 1.316 1.391 1.531 1.322 1.500 1.501 0.968 1.524 1.473
## [2961] 1.190 1.407 1.550 1.526 1.497 1.776 1.685 1.683 1.353 1.492
## [2971] 0.762 1.313 1.475 1.430 1.564 1.258 1.515 1.518 1.146 1.102
## [2981] 1.454 1.807 0.992 0.761 1.106 1.972 1.087 1.082 0.761 0.761
## [2991] 0.761 1.834 2.324 2.748 3.038 2.873 0.872 0.887 0.827 0.761
## [3001] 0.761 0.761 0.796 0.771 0.771 0.771 0.761 0.761 0.761 0.761
## [3011] 0.805 1.003 1.006 0.975 0.995 1.002 0.818 1.006 0.912 0.940
## [3021] 0.958 0.988 0.973 0.766 0.964 0.774 0.784 0.893 0.767 0.770
## [3031] 0.768 0.797 0.796 0.771 0.769 0.766 0.765 0.805 0.818 0.837
## [3041] 0.818 0.790 0.846 0.770 0.769 0.762 0.761 0.761 0.761 1.021
## [3051] 1.086 1.079 0.996 0.967 1.038 1.083 1.065 1.908 1.941 1.044
## [3061] 1.897 1.227 2.058 0.765 0.764 1.068 1.031 1.057 1.064 1.043
## [3071] 1.075 1.118 1.089 1.361 1.002 1.688 1.165 1.102 1.044 2.150
## [3081] 1.071 1.061 1.846 1.956 1.932 1.566 1.741 1.013 1.036 0.941
## [3091] 1.161 1.041 1.030 0.768 0.770 0.762 1.058 0.835 0.768 0.810
## [3101] 0.821 0.891 0.928 0.875 0.885 0.907 1.302 1.232 1.231 0.819
## [3111] 0.817 1.390 1.140 0.862 0.771 0.764 0.761 0.763 0.761 0.763
## [3121] 0.762 0.762 0.761 0.762 0.762 0.764 0.762 0.762 0.762 0.762
## [3131] 0.762 0.789 0.824 0.761 0.761 0.761 0.763 0.781 0.809 0.772
## [3141] 0.859 0.771 0.795 0.960 0.978 0.991 0.985 1.002 1.082 1.657
## [3151] 1.272 0.937 1.456 0.939 1.535 0.784 0.762 0.829 0.761 0.784
## [3161] 0.761 0.762 0.762 0.768 0.762 0.761 0.762 0.774 0.765 0.762
## [3171] 0.762 0.762 0.762 0.762 0.761 0.798 0.761 0.761 0.762 0.766
## [3181] 0.808 0.761 0.864 0.879 0.761 0.763 0.777 0.777 0.767 0.766
## [3191] 0.855 0.816 0.773 0.761 0.812 0.856 0.859 0.845 0.851 0.843
## [3201] 0.849 0.862 0.868 0.832 0.848 0.853 0.844 0.834 0.837 0.865
## [3211] 0.836 0.866 1.967 1.973 2.202 0.780 0.863 0.824 0.871 0.788
## [3221] 0.835 0.769 0.762 0.762 0.762 0.762 0.761 0.976 0.857 0.761
## [3231] 0.762 0.761 0.999 0.761 1.031 0.761 1.081 1.082 1.097 0.761
## [3241] 0.819 0.822 0.827 0.852 0.806 0.840 0.976 0.762 0.867 0.834
## [3251] 0.853 0.874 1.024 0.979 0.762 0.762 0.846 0.836 0.798 0.761
## [3261] 0.964 1.116 1.025 2.199 1.493 2.869 0.977 2.814 2.126 2.711
## [3271] 1.376 2.418 1.316 2.633 1.237 2.495 1.674 2.113 2.059 2.080
## [3281] 1.955 2.544 2.072 1.294 0.761 1.905 2.230 2.271 0.761 1.336
## [3291] 0.761 0.761 2.002 1.490 0.761 2.174 2.089 1.237 1.894 2.824
## [3301] 2.801 2.088 1.891 2.229 1.899 1.349 2.322 2.326 0.761 1.838
## [3311] 1.956 1.685 2.102 0.928 1.312 0.762 1.306 0.761 0.761 1.212
## [3321] 0.915 0.761 0.761 0.876 0.761 0.761 0.761 0.761 0.761 1.219
## [3331] 1.134 1.042 1.228 1.019 0.901 0.894 0.820 1.037 0.940 0.891
## [3341] 0.854 1.050 1.091 0.904 1.069 1.101 1.088 1.049 0.953 1.884
## [3351] 1.335 1.000 1.013 1.150 1.242 1.026 0.997 1.075 1.003 0.896
## [3361] 0.992 0.912 0.921 0.870 0.761 0.763 0.761 0.800 0.886 0.761
## [3371] 0.799 0.761 0.761 1.241 0.912 0.911 0.948 1.120 0.949 0.875
## [3381] 0.905 1.023 0.924 1.001 0.984 1.047 1.006 0.988 1.041 1.050
## [3391] 1.135 1.013 1.175 0.946 1.146 1.247 0.980 1.133 1.183 1.085
## [3401] 1.173 1.099 1.410 1.056 1.260 0.858 0.950 0.790 0.798 1.049
## [3411] 1.019 1.207 1.309 1.480 0.980 1.667 1.361 1.433 1.413 1.251
## [3421] 0.998 1.097 1.262 1.069 0.998 0.887 0.991 0.997 0.995 0.945
## [3431] 0.926 0.915 0.992 0.827 0.874 0.832 0.810 0.826 0.883 0.763
## [3441] 0.864 0.761 1.450 1.023 0.761 0.948 1.025 0.914 1.137 0.806
## [3451] 1.021 0.839 1.254 1.098 0.996 0.971 1.014 1.092 0.983 0.940
## [3461] 0.998 0.762 1.190 1.027 0.988 0.910 1.019 0.971 0.803 0.933
## [3471] 0.822 0.842 0.981 0.969 0.903 0.833 0.831 0.761 0.867 0.761
## [3481] 1.006 0.995 0.977 0.852 1.000 0.761 1.281 1.558 1.350 1.475
## [3491] 1.552 1.487 1.364 1.110 1.128 1.403 1.368 1.347 1.391 1.379
## [3501] 1.385 1.096 1.496 1.168 1.624 1.417 1.569 0.998 1.152 1.176
## [3511] 1.182 1.187 1.132 1.127 1.162 1.162 1.186 1.190 1.212 1.296
## [3521] 0.965 0.761 0.761 0.761 0.761 0.761 0.761 0.979 0.761 0.761
## [3531] 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3541] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3551] 0.762 1.191 0.762 0.762 0.762 1.167 0.762 0.762 0.762 0.762
## [3561] 1.367 0.762 0.762 0.762 1.579 0.762 0.762 1.054 0.762 0.762
## [3571] 0.921 0.762 1.021 0.762 0.762 0.762 0.762 1.387 0.762 0.762
## [3581] 0.762 1.347 0.762 0.762 0.762 0.762 0.762 1.122 0.762 0.762
## [3591] 0.762 1.710 0.762 0.762 1.389 0.762 0.762 0.762 0.823 0.762
## [3601] 0.762 0.762 0.762 0.762 0.762 0.818 0.762 0.762 0.762 0.762
## [3611] 0.762 0.762 0.762 0.762 1.217 0.762 0.762 0.762 0.823 0.762
## [3621] 0.762 0.762 1.115 0.762 0.762 0.762 0.762 0.994 0.762 0.762
## [3631] 0.813 0.762 0.762 0.762 0.904 0.762 0.762 0.762 0.762 0.762
## [3641] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3651] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3661] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3671] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3681] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3691] 0.762 0.762 0.762 0.762 1.269 1.088 0.841 0.762 0.762 0.762
## [3701] 1.048 0.762 0.870 0.762 0.762 0.762 1.201 0.762 1.065 0.762
## [3711] 1.168 0.762 1.398 0.762 1.237 0.762 1.243 0.762 0.762 1.250
## [3721] 0.762 0.762 1.049 0.762 0.762 0.762 1.015 0.762 0.762 0.898
## [3731] 0.762 0.762 0.909 0.762 0.762 0.839 0.762 0.762 0.762 0.875
## [3741] 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.762 0.762 0.762
## [3751] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3761] 0.762 0.762 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3771] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3781] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3791] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3801] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3811] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3821] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [3831] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 1.069 1.046
## [3841] 1.024 0.996 1.251 0.937 0.849 0.846 0.823 0.850 0.897 0.786
## [3851] 0.761 0.761 0.784 0.861 0.926 0.947 0.876 0.837 1.058 1.076
## [3861] 1.055 0.763 0.815 0.793 0.813 0.774 0.778 0.787 0.904 0.867
## [3871] 0.765 0.761 0.761 0.761 1.055 1.213 1.157 1.077 0.925 1.103
## [3881] 1.200 1.528 1.117 1.146 1.160 1.244 1.241 1.039 1.174 1.189
## [3891] 1.148 1.300 1.053 1.273 1.110 1.084 0.942 0.867 0.983 1.132
## [3901] 0.939 1.214 0.913 0.785 0.761 0.761 0.847 0.761 0.762 0.804
## [3911] 0.761 0.761 0.761 0.761 0.980 0.894 1.038 1.397 1.320 1.755
## [3921] 1.596 1.341 1.113 0.908 0.775 0.869 1.031 1.008 0.979 0.938
## [3931] 0.988 1.460 1.273 1.432 0.834 1.103 1.103 1.197 1.263 1.242
## [3941] 1.249 1.286 1.328 1.201 1.215 1.127 1.257 1.286 1.291 1.292
## [3951] 1.358 1.288 1.236 1.160 1.146 1.107 0.997 0.813 1.386 1.368
## [3961] 1.265 1.267 1.201 1.137 1.140 1.282 0.858 0.965 1.057 1.173
## [3971] 0.969 0.838 0.888 0.788 0.761 0.761 0.761 0.761 0.908 0.761
## [3981] 0.761 1.139 0.917 1.380 0.761 0.761 1.127 1.033 0.761 0.777
## [3991] 0.768 0.785 0.768 0.765 0.766 0.776 0.786 0.816 0.766 0.767
## [4001] 0.768 0.801 0.798 0.762 0.762 0.889 0.915 0.770 0.792 0.875
## [4011] 0.761 0.788 0.834 0.859 0.846 0.846 0.861 0.873 0.822 0.803
## [4021] 0.814 0.812 0.900 0.857 0.866 0.859 0.968 0.994 0.924 0.871
## [4031] 0.779 0.859 0.806 0.788 0.834 0.859 0.846 0.846 0.861 0.873
## [4041] 0.822 0.803 0.814 0.812 0.900 0.837 0.786 0.984 0.886 0.886
## [4051] 0.761 0.761 0.761 0.761 0.764 0.762 0.761 0.761 0.761 0.761
## [4061] 0.761 0.761 0.774 0.764 1.244 1.205 1.125 0.781 0.762 0.796
## [4071] 0.793 0.925 1.013 0.795 0.801 0.819 0.995 0.860 0.877 0.919
## [4081] 1.029 0.794 1.037 1.117 1.013 0.803 0.845 0.827 0.838 0.772
## [4091] 0.810 0.936 0.790 0.872 0.996 0.814 0.813 0.794 0.902 0.847
## [4101] 0.787 0.786 0.852 0.838 0.848 0.833 0.802 0.771 0.838 0.838
## [4111] 0.829 0.774 0.835 0.763 0.806 0.855 1.181 0.829 0.814 0.762
## [4121] 0.934 0.761 1.029 0.981 0.935 0.863 0.932 0.924 0.988 0.984
## [4131] 0.965 1.154 0.980 0.928 0.999 1.024 1.076 0.987 1.015 1.040
## [4141] 1.028 1.003 0.991 0.904 1.013 1.066 1.088 1.050 1.056 1.031
## [4151] 1.025 1.069 1.103 1.048 1.015 1.041 1.087 1.020 1.023 1.053
## [4161] 1.034 1.010 1.040 1.071 1.031 1.024 1.053 1.081 1.047 1.021
## [4171] 1.025 1.044 1.006 1.247 0.960 1.055 1.043 1.014 1.406 1.326
## [4181] 1.210 0.980 0.766 0.767 0.863 1.646 1.436 1.583 1.686 1.592
## [4191] 1.670 1.557 0.996 1.154 1.594 1.058 0.982 1.234 1.266 1.194
## [4201] 1.177 1.183 1.046 1.023 0.988 0.990 1.046 0.957 0.989 1.010
## [4211] 0.991 0.963 0.998 1.007 0.968 1.007 1.456 1.445 1.319 1.332
## [4221] 1.187 0.925 1.058 1.052 1.080 1.171 1.051 1.169 1.153 0.927
## [4231] 0.833 0.826 0.824 0.815 0.879 0.850 0.801 0.772 0.796 0.850
## [4241] 0.831 0.851 0.796 0.823 0.899 0.852 0.812 0.808 0.808 0.802
## [4251] 0.842 0.898 0.857 0.892 0.819 0.882 0.902 0.921 0.917 1.115
## [4261] 1.029 1.018 1.007 1.001 0.980 1.049 1.560 1.107 1.106 0.989
## [4271] 1.134 1.038 1.103 1.107 0.953 0.954 1.076 1.086 1.058 1.033
## [4281] 0.924 1.014 1.080 1.069 1.043 1.072 1.040 1.067 1.093 1.096
## [4291] 1.115 1.083 1.037 1.191 1.010 1.077 1.025 1.033 1.052 1.028
## [4301] 1.066 0.974 0.894 0.887 0.962 0.927 0.899 0.990 0.998 1.158
## [4311] 1.067 1.071 1.079 1.056 1.167 1.211 1.199 1.157 1.527 1.341
## [4321] 1.106 1.128 1.106 1.162 1.045 1.103 1.148 1.063 1.019 1.015
## [4331] 1.055 0.993 1.063 0.967 0.882 1.073 1.081 0.985 1.061 1.105
## [4341] 0.931 0.885 0.927 0.796 0.818 0.761 0.839 1.098 0.809 0.882
## [4351] 0.986 1.077 0.884 1.047 1.050 1.081 1.059 1.016 1.145 1.133
## [4361] 1.114 1.087 0.978 1.170 1.238 1.091 1.208 1.016 1.130 1.018
## [4371] 0.971 1.047 1.063 1.296 1.092 1.094 1.545 1.038 1.149 1.190
## [4381] 1.045 1.108 0.969 1.070 1.100 1.322 1.512 0.892 1.067 1.078
## [4391] 1.064 1.079 1.094 1.052 1.104 1.109 1.084 1.140 1.099 1.148
## [4401] 1.044 0.987 1.097 1.069 1.077 1.096 1.047 1.019 1.080 1.058
## [4411] 1.078 1.082 1.023 1.031 1.053 1.183 1.149 0.984 1.074 0.895
## [4421] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.836 0.848 0.772
## [4431] 0.860 0.761 0.761 0.761 0.761 0.762 0.761 0.766 0.929 1.050
## [4441] 0.973 1.012 1.053 0.982 1.059 1.048 1.060 1.127 1.090 1.065
## [4451] 1.064 1.090 1.097 1.021 1.024 1.034 1.032 1.059 1.044 0.986
## [4461] 1.149 1.024 1.010 1.068 0.988 0.936 0.988 0.792 0.761 0.869
## [4471] 0.761 0.861 1.139 0.859 1.046 1.087 1.145 0.858 0.853 0.897
## [4481] 1.020 0.957 0.916 0.981 1.155 0.796 0.989 0.966 1.036 0.843
## [4491] 0.962 0.972 1.063 0.951 0.931 0.853 0.864 0.762 0.817 0.909
## [4501] 0.864 0.761 0.761 0.761 0.889 0.888 0.961 0.969 0.900 0.793
## [4511] 0.846 0.862 0.874 0.954 0.918 1.068 1.041 1.148 1.345 1.063
## [4521] 0.848 1.036 1.027 0.875 0.801 0.860 0.935 0.868 0.844 0.915
## [4531] 0.830 0.924 0.917 1.071 1.279 1.024 1.001 1.043 0.959 0.895
## [4541] 0.914 1.015 0.875 0.838 0.845 0.903 1.054 0.921 0.888 0.828
## [4551] 0.820 0.886 0.987 0.899 1.043 0.772 0.873 0.922 1.187 1.262
## [4561] 1.119 1.111 1.219 1.180 1.238 1.118 0.964 1.424 1.199 1.609
## [4571] 1.287 1.686 1.551 1.183 1.272 1.668 1.772 1.436 1.214 0.902
## [4581] 1.156 1.285 1.212 1.153 1.173 1.165 1.238 1.248 1.243 1.012
## [4591] 1.162 1.520 1.334 1.367 1.422 1.459 1.367 1.183 1.174 1.421
## [4601] 1.459 1.431 1.332 1.287 1.358 1.246 1.203 1.209 1.156 1.328
## [4611] 1.589 1.239 1.267 1.214 1.102 1.113 1.051 0.997 1.037 0.990
## [4621] 0.992 0.910 1.244 1.001 1.131 1.097 1.320 1.221 1.062 0.847
## [4631] 0.857 0.987 1.010 0.883 0.861 1.040 0.874 0.950 0.921 0.984
## [4641] 0.918 0.893 0.878 1.020 1.040 0.970 0.915 0.895 0.941 0.995
## [4651] 1.089 0.901 0.855 1.047 0.852 1.102 0.906 0.941 0.809 1.107
## [4661] 0.942 0.911 0.891 1.142 1.168 1.016 1.168 0.938 0.891 1.024
## [4671] 0.997 0.932 1.026 1.108 1.040 0.987 1.059 0.961 1.032 1.077
## [4681] 1.435 1.133 1.027 1.300 0.976 1.050 0.856 1.023 0.985 1.040
## [4691] 0.962 0.948 0.974 0.953 0.881 0.970 0.839 0.886 0.958 1.061
## [4701] 0.852 1.106 0.962 0.963 1.044 1.167 1.026 1.008 1.012 0.981
## [4711] 0.892 0.978 0.993 1.042 0.937 1.020 1.124 0.973 1.120 1.161
## [4721] 1.096 1.220 1.240 1.151 1.155 1.106 1.112 1.147 1.193 1.049
## [4731] 1.108 1.075 1.191 1.507 1.224 1.147 2.007 1.434 1.293 1.184
## [4741] 1.056 1.288 1.262 1.347 1.283 1.238 1.436 1.322 1.411 1.314
## [4751] 1.403 1.287 1.365 1.217 1.267 1.280 1.228 1.246 1.221 1.188
## [4761] 1.112 1.226 1.164 1.147 1.261 1.202 1.274 1.024 1.315 1.323
## [4771] 1.068 1.184 1.212 0.921 1.335 1.237 1.118 0.805 1.308 0.961
## [4781] 0.926 1.184 1.042 1.354 1.167 1.566 1.378 1.115 0.801 1.003
## [4791] 0.959 1.386 0.761 1.213 1.049 1.182 1.319 1.146 1.021 0.966
## [4801] 0.930 1.153 1.134 1.029 1.175 1.002 1.174 0.940 0.998 0.941
## [4811] 1.023 1.112 1.323 1.396 1.276 1.137 1.284 1.234 0.982 1.172
## [4821] 0.993 1.538 1.308 1.135 1.296 1.140 1.310 1.285 1.288 1.261
## [4831] 1.276 1.143 1.255 1.148 1.347 1.452 1.678 1.690 1.529 1.638
## [4841] 1.278 1.379 1.415 1.137 1.877 1.131 1.432 1.386 1.427 1.363
## [4851] 1.466 1.417 1.354 1.036 1.049 1.543 1.525 1.533 1.519 1.563
## [4861] 1.546 1.562 1.467 1.570 0.997 1.253 1.199 1.115 1.159 1.209
## [4871] 1.179 1.210 1.211 1.188 1.396 1.212 0.849 0.765 0.766 0.762
## [4881] 0.768 0.779 0.779 0.785 0.774 0.776 0.794 0.819 0.858 0.840
## [4891] 0.792 0.787 0.829 0.957 0.768 0.787 0.774 0.811 0.803 0.774
## [4901] 0.784 0.767 0.762 0.762 0.913 0.824 0.816 0.786 0.844 0.812
## [4911] 0.817 0.796 0.785 0.828 0.785 0.819 0.764 0.866 0.879 1.027
## [4921] 0.973 1.048 0.989 1.017 1.053 0.986 1.017 1.004 1.050 1.035
## [4931] 1.050 0.985 1.016 1.103 1.020 0.998 1.024 0.985 1.133 1.063
## [4941] 0.868 1.014 0.945 0.951 1.014 1.069 0.999 1.050 1.071 1.154
## [4951] 1.108 1.040 1.004 1.040 0.775 1.002 1.019 1.047 1.055 1.030
## [4961] 1.068 1.055 1.013 1.045 0.942 1.042 1.063 0.970 1.056 1.042
## [4971] 1.029 1.033 0.798 0.892 1.004 1.004 1.045 1.056 1.112 1.058
## [4981] 1.090 1.098 1.124 1.117 0.827 0.934 1.089 1.096 1.107 1.084
## [4991] 1.058 1.068 1.041 1.079 1.061 1.138 1.051 1.130 1.142 1.309
## [5001] 0.969 1.321 1.844 1.522 0.827 1.031 1.068 1.282 0.810 0.796
## [5011] 0.777 0.801 1.748 0.795 0.771 0.806 0.777 0.771 0.787 0.775
## [5021] 0.795 0.801 0.775 0.777 0.775 0.773 0.791 0.799 1.064 0.761
## [5031] 0.761 0.761 0.767 0.900 0.925 0.943 0.762 0.762 0.762 0.762
## [5041] 0.762 0.762 0.762 0.766 0.766 0.763 0.761 0.761 0.761 0.763
## [5051] 0.848 0.761 0.761 0.761 0.761 0.761 0.773 0.761 0.761 0.761
## [5061] 0.761 0.761 0.761 0.827 0.761 0.761 0.797 0.793 0.812 0.807
## [5071] 0.846 0.761 0.761 0.761 0.761 0.819 0.765 0.860 0.762 0.762
## [5081] 0.765 0.762 0.766 0.765 0.776 0.770 0.851 0.796 1.031 1.309
## [5091] 0.789 0.952 0.912 0.851 0.761 0.873 0.824 0.781 0.761 0.761
## [5101] 0.761 0.761 0.761 0.761 0.761 0.849 0.811 0.791 0.788 0.825
## [5111] 0.808 0.825 0.807 1.097 0.761 0.761 0.761 1.038 1.413 0.852
## [5121] 0.892 0.837 0.762 0.829 0.762 0.763 0.783 0.772 0.784 0.804
## [5131] 0.791 0.761 0.858 0.932 1.026 0.761 1.356 0.766 0.761 0.761
## [5141] 0.761 0.761 0.761 0.761 0.761 0.761 0.865 0.828 0.801 0.824
## [5151] 0.811 0.850 0.823 1.087 1.137 0.761 1.149 1.172 1.081 1.424
## [5161] 0.964 0.784 0.770 0.774 0.771 0.770 0.819 0.765 0.773 0.773
## [5171] 0.770 0.777 0.790 0.764 0.776 0.768 0.764 0.855 0.785 0.766
## [5181] 0.765 0.767 0.802 0.792 0.780 0.801 0.788 0.772 0.774 0.762
## [5191] 0.768 0.762 0.875 0.771 0.791 0.762 0.762 0.857 0.772 0.809
## [5201] 0.781 0.799 0.781 0.762 0.769 0.778 0.776 0.771 0.766 0.763
## [5211] 0.768 0.773 0.769 0.770 0.765 0.779 0.788 0.762 0.770 0.850
## [5221] 0.962 0.763 0.762 0.762 0.762 0.904 0.765 0.767 0.763 0.761
## [5231] 0.773 0.763 0.763 0.771 0.872 0.769 0.814 0.808 0.799 0.796
## [5241] 0.902 0.810 0.794 0.786 0.803 0.788 0.789 0.811 0.816 0.813
## [5251] 0.808 0.814 0.821 0.827 0.808 0.810 0.777 0.839 0.805 0.791
## [5261] 0.797 0.790 0.769 0.762 0.762 0.762 0.829 0.801 0.772 0.806
## [5271] 0.786 0.776 0.780 0.782 0.766 0.819 0.779 0.773 0.768 0.787
## [5281] 0.837 1.310 0.868 0.786 0.784 0.762 0.765 0.762 0.762 0.794
## [5291] 0.761 0.761 0.761 0.761 1.292 0.761 1.032 1.113 1.209 1.051
## [5301] 0.761 0.761 0.865 0.761 0.761 0.761 0.761 0.761 0.761 0.791
## [5311] 0.798 0.797 0.813 0.773 0.775 0.815 1.117 0.761 0.761 0.973
## [5321] 1.116 0.979 0.837 0.761 0.765 0.793 0.773 0.769 0.783 0.782
## [5331] 0.772 0.781 0.799 0.779 0.819 0.856 0.846 1.170 0.805 0.952
## [5341] 0.873 1.444 0.820 0.785 0.805 0.797 0.807 0.822 0.817 0.838
## [5351] 0.793 0.841 0.837 0.853 0.792 0.873 0.859 0.853 0.893 0.872
## [5361] 0.875 0.847 0.823 0.840 0.877 0.902 2.365 2.365 0.819 0.793
## [5371] 0.766 0.776 0.761 0.765 0.769 0.809 0.761 0.761 0.761 0.761
## [5381] 0.762 0.766 1.064 0.978 1.035 0.981 1.072 0.956 1.010 0.979
## [5391] 0.885 0.765 0.783 0.798 0.762 0.761 0.809 0.856 0.863 0.761
## [5401] 0.910 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.824 0.864
## [5411] 0.865 0.810 1.147 0.993 1.097 0.761 1.116 1.157 0.761 0.761
## [5421] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [5431] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [5441] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.502 1.198
## [5451] 0.763 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.766
## [5461] 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761
## [5471] 0.761 0.761 0.761 0.821 0.836 0.789 0.761 0.823 0.761 0.761
## [5481] 0.761 0.761 0.761 0.973 1.149 1.173 0.775 0.789 0.762 0.762
## [5491] 0.837 0.762 1.154 1.690 0.848 0.828 0.828 0.761 0.761 0.761
## [5501] 0.761 0.761 0.761 1.299 0.824 0.829 0.791 0.773 0.762 0.771
## [5511] 0.762 0.762 0.762 0.762 0.761 0.761 0.802 0.773 1.337 0.761
## [5521] 1.174 1.241 0.761 1.299 0.761 0.764 0.761 0.761 0.761 0.761
## [5531] 0.761 0.761 0.919 0.975 0.871 0.875 1.079 0.936 1.098 0.851
## [5541] 0.761 0.863 1.006 0.761 0.761 0.761 0.762 0.762 0.762 0.762
## [5551] 0.762 0.762 0.762 0.762 0.762 0.761 2.038 0.764 0.764 0.762
## [5561] 0.762 1.855 1.163 0.993 1.021 1.720 0.762 0.794 0.762 0.762
## [5571] 0.762 0.762 0.803 0.803 1.244 0.761 1.166 0.761 0.761 0.762
## [5581] 0.762 0.761 0.761 0.762 0.762 0.762 1.783 1.126 1.016 1.482
## [5591] 1.638 1.998 1.679 1.699 1.722 1.628 1.096 1.700 1.762 2.446
## [5601] 1.908 1.306 1.140 1.265 0.761 0.761 0.761 0.761 0.761 2.147
## [5611] 1.410 1.222 1.176 1.856 0.762 0.762 0.762 0.762 0.762 0.762
## [5621] 0.762 0.762 0.761 1.117 0.761 0.762 0.761 0.761 0.761 0.761
## [5631] 0.761 1.260 0.920 1.009 1.144 1.121 1.276 1.084 1.128 1.155
## [5641] 1.173 1.134 1.177 1.176 1.184 1.226 1.289 1.319 1.056 1.435
## [5651] 1.130 1.011 1.000 1.065 3.222 1.734 1.109 1.031 0.770 1.812
## [5661] 1.164 2.597 1.365 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [5671] 1.132 1.133 1.283 0.761 0.761 0.772 0.791 0.818 0.761 0.771
## [5681] 0.762 0.815 0.822 0.769 0.819 0.778 0.795 0.844 0.900 0.762
## [5691] 1.113 1.174 1.039 1.192 1.199 1.074 0.788 0.792 0.981 0.911
## [5701] 1.143 1.300 1.211 0.829 0.885 0.761 0.787 0.800 0.893 1.244
## [5711] 2.174 1.353 1.169 1.025 1.259 1.098 1.031 1.159 1.128 1.349
## [5721] 1.140 1.066 1.193 1.180 1.276 1.295 1.149 1.144 1.058 1.203
## [5731] 1.011 1.215 1.272 1.287 1.244 1.354 1.327 1.272 3.344 1.294
## [5741] 1.400 1.296 1.374 0.761 1.203 0.815 0.792 0.762 1.010 1.095
## [5751] 1.080 1.158 1.207 0.771 0.836 1.002 0.833 0.977 0.762 0.797
## [5761] 0.985 0.813 0.763 0.762 0.796 0.881 0.914 0.832 1.117 0.764
## [5771] 1.333 0.761 1.072 0.761 0.764 0.762 0.955 0.995 1.184 1.133
## [5781] 0.999 1.279 1.100 0.916 0.770 0.885 0.996 1.058 0.957 1.035
## [5791] 1.135 0.774 0.768 0.771 0.762 0.762 0.768 0.878 0.887 0.761
## [5801] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.762 0.833
## [5811] 0.761 0.878 0.762 0.762 0.761 0.770 0.761 0.762 0.769 0.761
## [5821] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [5831] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [5841] 0.835 0.761 0.761 0.761 0.762 1.123 1.256 1.408 0.761 0.765
## [5851] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [5861] 0.761 0.761 0.764 0.761 0.995 1.062 0.762 0.895 0.761 0.761
## [5871] 0.761 0.871 0.761 1.631 1.184 1.121 1.075 1.267 3.512 0.761
## [5881] 0.761 0.761 0.761 0.761 0.761 0.804 0.761 0.761 0.814 0.813
## [5891] 0.761 0.761 0.979 0.761 0.761 1.631 0.761 0.897 0.762 0.761
## [5901] 2.297 1.059 0.762 1.158 0.761 0.761 1.074 1.253 1.264 1.245
## [5911] 1.204 0.832 1.157 1.120 1.103 0.987 0.906 1.059 1.066 0.884
## [5921] 0.999 1.054 0.783 0.786 0.782 1.187 1.268 0.786 0.766 1.231
## [5931] 0.762 0.768 0.761 0.761 0.761 1.292 1.327 1.145 1.052 1.152
## [5941] 1.124 1.108 1.051 1.598 1.568 1.727 1.552 1.684 0.761 0.761
## [5951] 1.596 1.775 0.761 1.452 1.707 1.493 1.488 1.192 0.761 0.761
## [5961] 0.762 0.762 0.761 0.761 0.762 0.762 1.220 1.941 1.927 0.761
## [5971] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761
## [5981] 0.761 0.761 0.761 0.761 2.037 1.246 1.844 0.761 0.762 0.762
## [5991] 0.761 0.761 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [6001] 0.762 0.762 0.761 1.952 0.761 0.762 1.549 2.321 0.761 0.761
## [6011] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.762 0.762 0.762
## [6021] 0.762 0.762 0.762 0.762 0.762 0.762 0.829 1.580 1.279 1.138
## [6031] 1.264 1.212 2.022 1.286 0.761 0.761 0.761 0.761 0.761 0.761
## [6041] 0.766 0.761 0.763 0.761 0.761 1.311 1.303 1.136 0.761 0.761
## [6051] 0.761 0.761 0.761 1.769 1.898 2.127 2.362 1.746 0.761 0.761
## [6061] 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 1.080
## [6071] 0.761 1.912 0.761 1.374 0.761 0.762 0.998 1.139 1.195 1.115
## [6081] 1.054 1.153 1.185 1.287 1.313 0.992 1.091 0.892 0.986 1.160
## [6091] 1.321 1.109 1.398 1.187 0.787 0.813 1.029 1.066 0.920 1.114
## [6101] 1.127 1.381 1.267 0.761 0.792 0.761 0.761 0.799 0.761 0.761
## [6111] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [6121] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 1.514 1.309
## [6131] 0.906 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.762 1.262
## [6141] 0.939 1.333 0.987 0.815 0.778 0.804 1.386 1.188 1.038 0.761
## [6151] 0.761 0.761 1.159 0.761 0.806 0.962 1.751 1.486 1.412 0.761
## [6161] 1.323 0.761 0.761 0.761 0.761 0.761 0.834 0.940 1.208 1.284
## [6171] 1.158 1.189 1.121 0.980 1.047 1.331 1.224 1.094 1.146 0.765
## [6181] 1.138 0.972 1.326 1.038 1.355 0.995 0.763 0.807 0.844 0.913
## [6191] 0.858 1.043 0.854 1.397 0.901 0.834 0.950 1.015 1.082 1.011
## [6201] 1.060 1.046 1.085 1.069 1.113 1.121 1.094 1.081 1.157 1.139
## [6211] 1.154 1.183 1.223 1.234 1.309 1.520 1.224 1.183 1.188 1.161
## [6221] 0.995 1.217 1.133 0.761 1.848 0.761 0.761 0.761 0.762 0.762
## [6231] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761 1.272 0.761
## [6241] 1.029 1.755 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [6251] 0.762 0.762 1.132 0.762 0.761 0.762 1.311 0.947 2.638 0.988
## [6261] 1.070 1.137 1.105 1.219 1.216 1.253 1.216 1.185 1.213 1.204
## [6271] 1.174 1.306 1.985 1.381 1.128 1.069 1.393 1.078 1.155 1.163
## [6281] 1.211 1.290 1.075 1.181 1.015 0.764 1.167 0.886 0.887 0.762
## [6291] 0.993 1.118 0.981 1.197 1.138 3.118 0.764 0.761 0.761 2.782
## [6301] 1.025 1.056 0.796 0.776 0.802 0.764 0.932 0.799 0.762 0.838
## [6311] 0.883 0.777 0.762 0.957 1.108 0.806 0.762 0.762 0.762 0.771
## [6321] 0.761 0.762 0.762 0.762 0.862 0.761 1.288 0.764 0.761 0.761
## [6331] 0.761 0.761 1.008 0.920 1.210 0.851 0.857 0.909 1.065 1.033
## [6341] 0.955 0.882 0.988 1.050 0.820 0.964 0.785 1.280 0.764 1.042
## [6351] 0.761 1.113 0.928 0.801 0.881 0.974 1.001 0.761 0.761 0.761
## [6361] 0.762 0.762 0.981 1.206 1.098 1.144 0.955 0.947 0.841 0.820
## [6371] 2.620 1.826 2.352 1.249 1.329 1.486 0.806 0.774 1.133 1.003
## [6381] 1.054 1.309 1.126 1.153 1.066 1.104 1.246 1.070 1.134 0.761
## [6391] 0.767 0.775 1.952 0.900 1.174 1.090 0.833 0.784 0.888 0.858
## [6401] 0.862 1.002 1.044 1.111 1.149 1.074 1.033 1.024 1.174 1.329
## [6411] 1.107 0.804 1.001 1.372 1.191 1.369 1.181 1.150 1.179 1.188
## [6421] 1.082 1.179 1.027 0.999 1.214 1.217 1.348 2.056 0.769 1.511
## [6431] 0.761 0.762 0.761 0.761 0.761 0.761 1.618 0.961 0.906 1.163
## [6441] 1.148 1.183 1.302 1.029 1.174 1.209 1.316 1.224 1.196 1.118
## [6451] 1.208 1.281 0.911 0.968 1.353 0.762 0.899 1.001 1.003 1.044
## [6461] 0.935 0.899 0.944 0.802 2.113 1.383 1.134 1.131 1.217 1.227
## [6471] 1.155 1.212 1.011 1.178 1.196 0.761 1.213 1.215 1.237 1.130
## [6481] 0.824 0.963 1.156 1.012 0.800 0.811 1.129 1.245 0.877 0.958
## [6491] 1.008 1.130 3.690 0.933 0.765 1.435 1.226 2.928 1.320 0.813
## [6501] 0.852 0.899 0.761 0.762 0.814 0.805 0.827 0.798 0.955 0.837
## [6511] 1.860 1.398 0.937 0.874 1.348 0.982 0.761 0.769 0.786 0.761
## [6521] 0.761 0.761 0.761 0.762 0.760 0.901 0.762 0.761 0.761 0.761
## [6531] 0.761 0.761 0.761 0.761 0.853 0.943 0.947 1.190 1.863 3.208
## [6541] 1.336 0.920 1.005 3.019 0.761 0.788 0.766 0.943 1.014 0.761
## [6551] 0.762 0.762 0.760 1.100 0.761 1.164 1.103 1.140 1.080 1.036
## [6561] 0.818 0.762 0.780 0.809 0.789 0.818 0.965 0.762 2.057 1.061
## [6571] 0.892 1.023 1.267 0.992 0.957 0.966 0.892 0.966 1.178 1.036
## [6581] 1.273 1.156 1.029 1.007 1.225 1.943 1.217 0.762 0.762 0.762
## [6591] 0.762 0.762 0.762 0.761 0.762 0.761 0.762 0.762 0.762 0.762
## [6601] 0.762 1.221 0.761 1.404 0.761 0.762 0.762 0.762 0.762 0.851
## [6611] 1.024 0.762 1.211 0.782 0.762 1.767 1.202 1.076 1.201 1.061
## [6621] 0.761 1.113 3.092 1.218 1.142 1.483 0.761 0.761 0.761 0.761
## [6631] 0.761 0.761 0.761 0.943 0.761 1.759 0.866 1.112 0.761 0.816
## [6641] 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762 0.762
## [6651] 0.762 0.762 0.762 0.764 0.762 1.478 0.762 0.761 0.762 0.762
## [6661] 0.762 0.762 0.762 0.762 0.761 0.764 2.367 0.761 0.873 0.762
## [6671] 1.313 1.866 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [6681] 0.764 0.762 0.760 0.762 0.761 0.764 0.787 0.796 0.761 0.864
## [6691] 1.248 1.009 1.163 0.831 1.703 1.238 0.761 0.762 0.962 1.368
## [6701] 1.177 1.950 1.052 0.762 0.924 0.932 0.846 0.762 0.897 0.762
## [6711] 0.905 0.761 0.761 0.762 0.762 0.995 0.761 0.761 0.761 0.761
## [6721] 0.761 0.902 0.761 0.761 0.761 0.777 0.761 1.447 1.470 0.761
## [6731] 0.761 0.761 1.857 0.762 1.585 0.775 0.761 0.761 0.765 0.762
## [6741] 0.762 0.761 0.763 0.765 0.762 0.762 0.762 0.762 0.765 0.765
## [6751] 0.762 0.762 0.762 0.765 0.765 0.762 0.762 0.761 0.766 0.765
## [6761] 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762 0.765 0.761
## [6771] 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762 0.761 0.765
## [6781] 0.765 0.762 0.762 0.762 0.766 0.785 0.825 0.762 0.762 0.761
## [6791] 0.765 0.791 0.762 0.762 0.761 0.766 0.870 0.762 0.762 0.761
## [6801] 0.765 0.765 0.762 0.762 0.761 0.765 0.765 0.762 0.765 0.808
## [6811] 0.765 0.765 0.762 0.935 0.761 0.765 0.761 0.762 0.762 0.761
## [6821] 0.765 0.765 0.762 0.762 0.761 0.765 0.765 0.762 0.761 0.761
## [6831] 0.765 0.765 0.762 0.762 0.761 0.765 0.762 0.762 0.761 0.765
## [6841] 0.762 0.761 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.761
## [6851] 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.762 0.761 0.765
## [6861] 0.762 0.762 0.761 0.765 0.762 0.761 0.766 0.765 0.762 0.762
## [6871] 0.765 0.761 0.765 0.762 0.762 0.765 0.762 0.765 0.765 0.762
## [6881] 0.765 0.765 0.762 0.765 0.765 0.762 0.761 0.765 0.761 0.765
## [6891] 0.762 0.762 0.765 0.761 0.787 0.762 0.786 0.765 0.761 0.765
## [6901] 0.762 0.762 0.765 0.761 0.765 0.762 0.761 0.765 0.761 0.765
## [6911] 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761
## [6921] 0.761 0.765 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762
## [6931] 0.765 0.761 0.761 0.765 0.762 0.762 0.761 0.761 0.761 0.765
## [6941] 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761
## [6951] 0.761 0.761 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762
## [6961] 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761 0.762 0.765
## [6971] 0.762 0.762 0.762 0.761 0.761 0.763 0.762 0.762 0.761 0.761
## [6981] 0.761 0.765 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762
## [6991] 0.762 0.761 0.762 0.761 0.762 0.762 0.765 0.761 0.767 0.765
## [7001] 0.762 0.762 0.763 0.766 0.765 0.762 0.762 0.764 0.765 0.765
## [7011] 0.762 0.762 0.764 0.765 0.765 0.762 0.762 0.761 0.767 0.765
## [7021] 0.762 0.762 0.763 0.765 0.765 0.762 0.762 0.764 0.767 0.761
## [7031] 0.762 0.762 0.762 0.765 0.765 0.762 0.762 0.761 0.766 0.764
## [7041] 0.762 0.762 0.765 0.763 0.763 0.762 0.762 0.762 0.765 0.762
## [7051] 0.762 0.762 0.763 0.766 0.762 0.762 0.762 0.761 0.765 0.765
## [7061] 0.762 0.762 0.761 0.765 0.765 0.762 0.761 0.761 0.766 0.765
## [7071] 0.762 0.761 0.761 0.766 0.762 0.762 0.762 0.761 0.765 0.765
## [7081] 0.762 0.762 0.761 0.765 0.765 0.762 0.761 0.761 0.765 0.765
## [7091] 0.762 0.762 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.761
## [7101] 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.761 0.761 0.945
## [7111] 0.762 0.762 0.761 0.892 1.161 0.762 0.762 0.880 1.158 1.270
## [7121] 1.169 0.833 0.821 1.011 1.321 0.827 0.854 1.288 0.995 0.956
## [7131] 1.261 0.946 1.296 0.899 1.205 0.955 1.175 0.832 1.298 0.839
## [7141] 1.316 0.873 1.197 0.853 0.767 1.146 0.825 0.762 0.765 1.226
## [7151] 0.765 0.762 0.815 0.768 0.767 0.765 0.762 0.765 0.762 0.762
## [7161] 0.761 0.765 0.761 0.765 0.762 0.765 0.761 0.765 0.761 0.765
## [7171] 0.762 0.765 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.761
## [7181] 0.761 0.765 0.762 0.762 0.761 0.762 0.765 0.762 0.761 0.761
## [7191] 0.762 0.765 0.762 0.761 0.761 0.762 0.765 0.762 0.762 0.761
## [7201] 0.763 0.765 0.762 0.762 0.761 0.762 0.761 0.762 0.762 0.761
## [7211] 0.763 0.765 0.762 0.762 0.761 0.763 0.765 0.762 0.762 0.763
## [7221] 0.764 0.765 0.762 0.762 0.765 0.762 0.764 0.762 0.761 0.762
## [7231] 0.765 0.765 0.762 0.762 0.763 0.764 0.765 0.762 0.762 0.762
## [7241] 0.804 0.761 0.762 0.768 0.761 0.762 0.765 0.762 0.762 0.762
## [7251] 0.761 0.764 0.765 0.762 0.762 0.762 0.762 0.765 0.765 0.762
## [7261] 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762 0.761 0.765
## [7271] 0.765 0.762 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762
## [7281] 0.761 0.765 0.761 0.762 0.762 0.762 0.761 0.765 0.765 0.762
## [7291] 0.762 0.762 0.761 0.765 0.765 0.762 0.762 0.762 0.766 0.762
## [7301] 0.762 0.762 0.762 0.762 0.761 0.765 0.762 0.762 0.762 0.762
## [7311] 0.762 0.765 0.762 0.762 0.762 0.762 0.761 0.765 0.765 0.762
## [7321] 0.762 0.805 0.761 0.765 0.765 0.762 0.762 0.761 0.761 0.765
## [7331] 0.765 0.762 0.762 0.784 0.761 0.765 0.761 0.762 0.762 0.762
## [7341] 0.761 0.765 0.765 0.762 0.762 0.762 0.761 0.765 0.765 0.762
## [7351] 0.762 0.761 0.783 0.765 0.765 0.762 0.762 0.762 0.761 0.765
## [7361] 0.762 0.762 0.762 0.761 0.765 0.762 0.762 0.761 0.761 0.765
## [7371] 0.762 0.762 0.780 0.761 0.765 0.762 0.761 0.761 0.765 0.762
## [7381] 0.762 0.761 0.765 0.762 0.762 0.761 0.765 0.762 0.762 0.761
## [7391] 0.765 0.762 0.761 0.781 0.765 0.762 0.762 0.765 0.761 0.765
## [7401] 0.762 0.762 0.765 0.762 0.765 0.761 0.765 0.762 0.765 0.765
## [7411] 0.762 0.765 0.765 0.762 0.761 0.765 0.761 0.765 0.762 0.762
## [7421] 0.765 0.762 0.762 0.762 0.761 0.765 0.761 0.765 0.762 0.762
## [7431] 0.765 0.761 0.765 0.762 0.761 0.765 0.761 0.765 0.762 0.762
## [7441] 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761 0.762 0.765
## [7451] 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.761 0.761
## [7461] 0.761 0.765 0.762 0.762 0.761 0.761 0.761 0.765 0.762 0.762
## [7471] 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761 0.762 0.761
## [7481] 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762 0.762 0.761
## [7491] 0.761 0.765 0.762 0.762 0.762 0.761 0.761 0.765 0.762 0.762
## [7501] 0.762 0.769 0.762 0.763 0.762 0.762 0.762 0.761 0.762 0.765
## [7511] 0.762 0.762 0.762 0.761 0.762 0.765 0.762 0.762 0.762 0.761
## [7521] 0.773 0.761 0.762 0.762 0.764 0.762 0.943 0.763 0.763 0.795
## [7531] 0.762 0.970 0.763 0.788 0.950 0.907 0.913 0.763 0.763 0.827
## [7541] 0.764 0.773 0.763 0.763 0.763 0.826 0.765 0.763 0.763 1.340
## [7551] 0.855 0.797 0.763 0.952 0.872 0.815 0.862 0.763 0.763 0.832
## [7561] 0.874 0.841 0.763 0.763 0.787 0.906 0.810 0.763 0.763 0.848
## [7571] 0.762 0.849 0.763 0.763 0.981 0.824 0.762 0.763 0.763 0.763
## [7581] 0.843 0.764 0.763 0.865 0.763 0.803 0.764 0.763 0.763 0.845
## [7591] 0.772 0.764 0.763 0.763 0.761 0.764 0.785 0.763 0.763 0.991
## [7601] 0.845 0.801 0.763 0.763 0.792 0.901 0.764 0.763 0.763 0.802
## [7611] 0.762 0.819 0.764 0.763 0.763 0.761 0.762 0.842 0.770 0.763
## [7621] 0.966 0.811 0.826 0.761 0.763 0.763 0.763 0.765 0.763 0.763
## [7631] 0.762 0.912 0.763 0.763 0.762 0.912 0.763 0.762 1.035 0.763
## [7641] 0.885 0.762 1.036 0.761 0.763 0.762 1.081 0.762 1.038 1.457
## [7651] 0.857 0.762 0.973 0.762 1.036 0.946 0.766 0.762 1.072 1.054
## [7661] 1.059 0.909 0.763 0.762 1.059 0.761 1.018 0.890 0.762 1.058
## [7671] 0.761 1.044 0.815 0.924 1.006 0.761 1.034 0.828 0.870 1.059
## [7681] 0.761 0.970 0.886 0.987 1.019 0.761 1.002 0.917 0.851 0.910
## [7691] 0.761 0.881 0.869 1.047 0.764 0.762 0.920 0.808 0.806 0.764
## [7701] 0.762 0.762 0.788 0.763 0.764 0.853 0.762 0.765 0.764 0.762
## [7711] 0.825 0.760 0.763 0.764 0.762 0.832 0.763 0.766 0.995 0.762
## [7721] 0.764 0.763 0.763 1.048 0.762 0.768 0.763 0.763 0.932 0.762
## [7731] 0.827 0.763 0.763 0.929 0.762 0.791 0.763 0.763 0.930 0.762
## [7741] 0.799 0.763 0.763 0.763 0.762 0.790 0.763 0.763 0.765 0.762
## [7751] 0.761 0.763 0.763 0.763 0.762 0.764 0.763 0.763 0.788 0.761
## [7761] 0.764 0.763 0.763 0.896 0.762 0.764 0.763 0.763 0.858 0.762
## [7771] 0.848 0.763 0.873 0.902 0.762 1.036 0.763 0.783 0.843 0.762
## [7781] 0.909 0.763 0.763 0.872 0.762 0.906 0.763 0.763 0.762 0.763
## [7791] 0.762 1.034 1.049 1.046 0.858 0.762 1.044 1.049 1.023 0.876
## [7801] 0.762 1.072 1.053 1.042 1.038 1.032 0.896 1.044 0.983 0.976
## [7811] 1.084 0.762 0.951 1.052 0.987 1.015 1.046 0.993 1.061 0.987
## [7821] 1.031 1.077 0.762 1.013 1.037 1.052 1.018 1.056 0.855 1.059
## [7831] 1.036 1.024 1.073 0.762 0.764 1.039 1.027 1.002 1.044 0.997
## [7841] 0.804 1.023 0.989 0.993 1.087 0.762 0.986 1.046 1.002 1.057
## [7851] 1.087 0.766 1.040 1.051 1.004 0.925 0.764 0.898 1.042 1.046
## [7861] 0.874 0.775 1.022 1.009 1.046 0.879 0.884 1.005 1.002 1.042
## [7871] 0.876 0.764 0.969 0.954 0.956 0.864 0.816 0.998 0.893 0.953
## [7881] 0.833 0.818 1.061 0.984 1.029 0.908 0.767 1.042 1.054 1.050
## [7891] 0.911 0.765 0.761 1.021 1.020 0.907 0.762 0.782 0.761 1.042
## [7901] 1.050 0.853 0.768 0.761 1.039 1.090 0.839 0.764 0.761 1.011
## [7911] 0.762 0.989 0.762 0.799 0.761 1.006 0.762 0.910 0.762 0.943
## [7921] 5.591 1.031 0.762 1.005 0.762 0.944 1.637 1.004 0.762 0.942
## [7931] 0.762 0.971 0.976 1.015 0.762 1.080 0.761 0.997 0.976 0.975
## [7941] 0.762 1.018 0.761 0.985 1.007 0.762 1.015 0.761 1.014 1.014
## [7951] 0.976 0.913 0.761 0.999 1.003 1.036 1.028 0.761 1.049 1.030
## [7961] 0.998 0.945 0.761 1.025 1.023 1.025 1.057 0.761 1.040 1.050
## [7971] 0.993 0.935 0.762 0.806 1.031 1.013 0.908 0.920 1.040 1.008
## [7981] 0.982 0.950 0.762 0.975 0.923 0.762 0.970 1.050 1.014 0.770
## [7991] 0.960 0.762 0.906 1.053 1.006 1.059 0.762 1.009 1.052 1.006
## [8001] 1.085 0.762 1.001 1.039 0.984 1.080 0.762 0.762 0.917 1.046
## [8011] 1.032 1.073 0.762 0.762 1.051 1.044 1.001 1.061 0.762 0.762
## [8021] 1.043 1.050 1.005 1.047 0.762 1.061 1.032 0.910 1.079 0.762
## [8031] 0.762 0.822 1.009 1.009 1.049 0.762 0.762 0.938 1.055 0.995
## [8041] 1.048 0.761 0.987 1.030 1.046 0.969 0.762 0.858 1.028 1.045
## [8051] 1.044 0.762 0.762 0.851 1.040 1.057 1.082 0.762 0.762 1.023
## [8061] 1.029 1.021 1.051 0.762 1.039 1.033 1.021 1.016 0.762 1.018
## [8071] 1.032 1.007 0.762 0.818 0.818 0.792 0.762 0.832 0.846 0.762
## [8081] 0.811 0.824 0.762 0.761 0.817 0.762 0.817 0.816 0.762 0.828
## [8091] 0.818 0.762 0.846 0.802 0.762 0.834 0.817 0.762 0.828 0.827
## [8101] 0.828 0.819 0.762 0.807 0.823 0.762 0.813 0.817 0.762 0.838
## [8111] 0.831 0.761 0.849 0.813 0.761 0.847 0.821 0.762 0.835 0.830
## [8121] 0.762 0.822 0.818 0.761 0.830 0.831 0.762 0.828 0.816 0.762
## [8131] 0.822 0.823 0.761 0.830 0.815 0.762 0.800 0.810 0.761 0.830
## [8141] 0.818 0.762 0.817 0.806 0.762 0.809 0.812 0.762 0.807 0.805
## [8151] 0.761 0.826 0.828 0.762 0.761 0.802 0.761 0.761 0.821 0.762
## [8161] 0.761 0.818 0.864 0.761 0.845 0.761 0.828 0.761 0.781 0.815
## [8171] 0.856 0.803 0.785 0.801 0.824 0.800 0.807 0.811 0.804 0.798
## [8181] 0.812 0.805 0.796 0.813 0.806 0.830 0.812 0.821 0.803 0.805
## [8191] 0.767 0.835 0.834 0.810 0.847 0.799 0.803 0.797 0.804 0.802
## [8201] 0.782 0.805 0.815 0.817 0.848 0.817 0.809 0.761 0.762 0.761
## [8211] 0.762 0.762 0.762 0.762 0.762 0.761 0.762 0.761 0.762 0.761
## [8221] 0.762 0.761 0.762 0.761 0.762 0.766 0.762 0.761 0.762 0.763
## [8231] 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [8241] 0.762 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.761 0.761
## [8251] 0.762 0.763 0.761 0.761 0.762 0.761 0.762 0.761 0.762 0.761
## [8261] 0.761 0.798 0.762 0.761 0.761 0.927 0.762 0.765 0.766 0.765
## [8271] 0.761 0.767 0.762 0.765 0.761 0.766 0.762 0.765 0.761 0.765
## [8281] 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [8291] 0.762 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762 0.762
## [8301] 0.762 0.761 0.761 0.765 0.762 0.761 0.762 0.761 0.761 0.784
## [8311] 0.783 0.762 0.762 0.791 0.815 0.828 0.762 0.762 0.776 0.795
## [8321] 0.814 0.762 0.762 0.830 0.822 0.773 0.762 0.762 0.761 0.845
## [8331] 0.798 0.762 0.762 0.776 0.846 0.773 0.762 0.762 0.764 0.834
## [8341] 0.761 0.762 0.762 0.804 0.815 0.852 0.762 0.762 0.772 0.824
## [8351] 0.858 0.762 0.762 0.812 0.822 0.839 0.762 0.762 0.815 0.835
## [8361] 0.847 0.762 0.762 0.837 0.858 0.797 0.762 0.762 0.825 0.842
## [8371] 0.859 0.762 0.762 0.845 0.843 0.860 0.762 0.761 0.859 0.780
## [8381] 0.843 0.762 0.761 0.852 0.823 0.838 0.762 0.854 0.847 0.856
## [8391] 0.762 0.815 0.849 0.840 0.761 0.822 0.827 0.830 0.762 0.826
## [8401] 0.812 0.761 0.762 0.827 0.826 0.761 0.761 0.818 0.846 0.761
## [8411] 0.762 0.812 0.851 0.761 0.761 0.841 1.072 0.761 0.762 0.825
## [8421] 0.831 0.766 0.762 0.838 1.117 1.119 0.762 1.316 1.195 1.073
## [8431] 1.233 1.367 1.512 1.272 1.201 1.280 1.487 1.206 1.136 1.311
## [8441] 1.310 1.081 1.224 1.307 1.186 1.325 1.330 1.192 1.296 1.321
## [8451] 1.230 0.859 1.289 0.918 0.761 0.851 0.761 0.835 0.855 0.847
## [8461] 0.762 0.840 0.825 0.850 0.761 0.850 0.826 0.842 0.762 0.841
## [8471] 0.830 0.851 0.761 0.856 0.826 0.861 0.762 0.762 0.853 0.836
## [8481] 0.827 0.762 0.762 0.857 0.812 0.840 0.762 0.762 0.854 0.821
## [8491] 0.863 0.762 0.761 0.845 0.835 0.863 0.762 0.761 0.851 0.832
## [8501] 0.856 0.762 0.762 0.843 0.839 0.861 0.762 0.762 0.847 0.841
## [8511] 0.763 0.762 0.762 0.856 0.850 0.772 0.762 0.762 0.830 0.837
## [8521] 0.816 0.762 0.762 0.822 0.808 0.808 0.762 0.762 0.839 0.808
## [8531] 0.849 0.762 0.761 0.823 0.835 0.851 0.762 0.762 0.800 0.785
## [8541] 0.819 0.762 0.762 0.785 0.820 0.765 0.762 0.783 0.761 0.762
## [8551] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [8561] 0.762 0.762 0.761 0.761 0.762 0.762 1.706 0.761 0.762 0.761
## [8571] 0.762 0.761 0.762 0.762 0.762 0.761 0.761 0.762 0.762 0.762
## [8581] 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.761 0.761 0.761
## [8591] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8601] 0.761 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [8611] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [8621] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [8631] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [8641] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [8651] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8661] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [8671] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.762 0.761
## [8681] 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8691] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8701] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8711] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [8721] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8731] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8741] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8751] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [8761] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761
## [8771] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 1.579 1.875
## [8781] 0.761 1.100 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [8791] 0.761 0.805 0.795 0.787 0.774 0.765 0.769 0.772 0.768 0.777
## [8801] 0.775 0.767 0.775 0.770 0.772 0.776 0.784 0.776 0.784 0.776
## [8811] 0.784 0.768 0.770 0.775 0.775 0.770 0.768 0.773 0.771 0.771
## [8821] 0.772 0.771 0.781 0.773 0.768 0.766 0.778 0.771 0.803 0.771
## [8831] 0.781 0.778 0.771 0.770 0.771 0.780 0.775 0.780 0.772 0.780
## [8841] 0.771 0.768 0.770 0.775 0.781 0.785 0.775 0.771 0.767 0.773
## [8851] 0.771 0.770 0.774 0.773 0.772 0.769 0.770 0.766 0.768 0.775
## [8861] 0.770 0.782 0.768 0.768 0.772 0.769 0.779 0.772 0.775 0.781
## [8871] 0.778 0.787 0.784 0.779 0.771 0.779 0.791 0.786 0.773 0.777
## [8881] 0.781 0.782 0.784 0.783 0.773 0.775 0.783 0.782 0.773 0.778
## [8891] 0.774 0.768 0.771 0.768 0.780 0.776 0.782 0.794 0.785 0.781
## [8901] 0.783 0.782 0.792 0.788 0.786 0.787 0.776 0.774 0.764 0.766
## [8911] 0.766 0.763 0.766 0.768 0.768 0.767 0.766 0.769 0.770 0.770
## [8921] 0.771 0.766 0.764 0.779 0.831 0.819 0.768 0.779 0.775 0.769
## [8931] 0.770 0.767 0.767 0.770 0.767 0.767 0.769 0.769 0.769 0.765
## [8941] 0.766 0.774 0.772 0.776 0.782 0.773 0.769 0.772 0.770 0.770
## [8951] 0.771 0.771 0.769 0.770 0.767 0.768 0.817 0.778 0.777 0.768
## [8961] 0.779 0.778 0.775 0.770 0.780 0.779 0.785 0.779 0.770 0.777
## [8971] 0.772 0.776 0.783 0.781 0.775 0.774 0.776 0.771 0.785 0.797
## [8981] 0.795 0.775 0.772 0.774 0.775 0.783 0.774 0.776 0.785 0.785
## [8991] 0.786 0.785 0.778 0.777 0.773 0.779 0.774 0.770 0.778 0.780
## [9001] 0.770 0.776 0.774 0.772 0.773 0.771 0.770 0.768 0.773 0.770
## [9011] 0.770 0.773 0.774 0.765 0.769 0.766 0.766 0.777 0.777 0.773
## [9021] 0.768 0.772 0.769 0.778 0.776 0.778 0.773 0.777 0.773 0.771
## [9031] 0.772 0.765 0.765 0.765 0.764 0.764 0.763 0.764 0.765 0.763
## [9041] 0.765 0.763 0.782 1.418 0.763 0.763 0.764 0.765 0.763 0.763
## [9051] 0.764 0.763 0.766 0.763 0.764 0.766 0.765 0.769 0.764 0.765
## [9061] 0.765 0.764 0.765 0.767 0.765 0.767 0.764 0.765 0.762 0.765
## [9071] 0.764 0.764 0.764 0.764 0.765 0.767 0.766 0.768 0.767 0.764
## [9081] 0.766 0.764 0.765 0.764 0.767 0.767 0.765 0.763 0.764 0.769
## [9091] 0.770 0.766 0.763 0.767 0.765 0.764 0.763 0.764 0.768 0.765
## [9101] 0.766 0.766 0.767 0.768 0.763 0.764 0.765 0.766 0.763 0.763
## [9111] 0.763 0.763 0.763 0.764 0.765 0.764 0.763 0.765 0.768 0.765
## [9121] 0.763 0.764 0.764 0.765 0.763 0.764 0.783 0.771 0.792 0.763
## [9131] 0.773 0.778 0.776 0.767 0.767 0.766 0.766 0.764 0.770 0.768
## [9141] 0.779 0.808 0.821 0.799 0.776 0.801 0.818 0.808 0.815 0.811
## [9151] 0.773 0.770 0.766 0.767 0.773 0.768 0.776 0.774 0.772 0.773
## [9161] 0.773 0.765 0.762 0.763 0.768 0.767 0.763 0.762 0.764 0.763
## [9171] 0.763 0.767 0.765 0.764 0.763 0.766 0.763 0.765 0.763 0.764
## [9181] 0.764 0.763 0.768 0.765 0.765 0.765 0.766 0.763 0.763 0.764
## [9191] 0.765 0.763 0.765 0.765 0.763 0.772 0.764 0.764 0.764 0.763
## [9201] 0.763 0.763 0.765 0.764 0.764 0.765 0.764 0.763 0.763 0.762
## [9211] 0.762 0.762 0.762 0.762 0.763 0.762 0.763 0.764 0.763 0.763
## [9221] 0.763 0.762 0.763 0.763 0.764 0.764 0.763 0.763 0.770 0.776
## [9231] 0.764 0.762 0.784 0.801 0.782 0.786 0.803 0.780 0.818 0.836
## [9241] 0.808 0.787 0.798 0.833 0.803 0.779 0.786 0.783 0.763 0.781
## [9251] 0.763 0.779 0.772 0.772 0.784 0.781 0.771 0.774 0.794 0.790
## [9261] 0.774 0.770 0.790 0.767 0.765 0.764 0.814 0.766 0.790 0.762
## [9271] 0.787 0.764 0.779 0.795 0.771 0.788 0.792 0.807 0.782 0.818
## [9281] 0.834 0.815 0.817 0.811 0.776 0.764 0.781 0.763 0.773 0.767
## [9291] 0.772 0.763 0.762 0.785 0.768 0.776 0.771 0.763 0.762 0.762
## [9301] 0.764 0.763 0.762 0.764 0.762 0.766 0.762 0.871 0.763 0.855
## [9311] 0.779 0.848 0.821 0.831 0.811 0.777 0.784 0.902 0.764 0.766
## [9321] 0.761 0.762 0.764 0.778 0.762 0.765 0.762 0.766 0.804 0.768
## [9331] 0.768 0.784 0.767 0.765 0.777 0.774 0.780 0.800 0.762 0.761
## [9341] 0.766 0.761 0.782 0.791 0.810 0.792 0.797 0.776 0.771 0.786
## [9351] 0.788 0.763 0.800 0.801 0.770 0.763 0.765 0.768 0.776 0.773
## [9361] 0.769 0.797 0.772 0.776 0.761 0.774 0.794 0.770 0.773 0.798
## [9371] 0.761 0.765 0.807 0.805 0.768 0.772 0.809 0.805 0.762 0.770
## [9381] 0.773 0.762 0.775 0.764 0.784 0.761 0.792 0.782 0.772 0.793
## [9391] 0.770 0.780 0.764 0.788 0.803 0.774 0.770 0.775 0.766 0.763
## [9401] 0.775 0.779 0.763 0.776 0.782 0.764 0.778 0.782 0.762 0.774
## [9411] 0.790 0.762 0.762 0.762 0.762 0.771 0.762 0.767 0.762 0.766
## [9421] 0.762 0.761 0.762 0.761 0.761 0.894 0.921 1.057 1.107 1.097
## [9431] 1.068 1.101 1.132 1.062 0.843 0.762 0.762 0.762 0.762 0.762
## [9441] 0.764 0.761 0.763 0.762 0.762 0.762 0.762 0.772 0.769 0.784
## [9451] 0.770 0.770 0.769 0.774 0.761 0.761 0.761 0.763 0.763 0.763
## [9461] 0.763 0.763 0.761 0.761 0.953 0.923 0.924 0.963 0.801 0.762
## [9471] 0.893 0.829 0.762 0.809 0.770 0.772 0.794 0.801 0.779 0.781
## [9481] 0.762 0.769 0.762 0.762 0.762 0.762 0.761 0.762 0.762 0.761
## [9491] 0.761 0.761 0.761 0.761 0.776 0.773 0.771 0.776 0.771 0.783
## [9501] 0.772 0.771 0.772 0.771 0.772 0.766 0.772 0.770 0.769 0.766
## [9511] 0.775 0.768 0.774 0.777 0.772 1.023 0.979 0.932 0.849 0.975
## [9521] 0.940 0.821 0.772 0.783 0.773 0.785 0.794 0.778 0.778 0.777
## [9531] 0.778 0.780 0.792 0.814 0.798 0.883 0.849 0.835 0.848 0.902
## [9541] 0.872 0.897 0.839 0.845 0.839 0.845 0.857 0.852 0.837 0.848
## [9551] 0.825 0.814 0.843 0.822 0.796 0.832 0.798 0.832 0.816 0.811
## [9561] 0.804 0.784 0.816 0.789 0.785 0.782 0.799 0.787 0.799 0.796
## [9571] 0.789 0.780 0.775 0.796 0.837 0.761 0.789 0.876 0.766 0.797
## [9581] 0.765 0.836 0.818 0.822 0.762 0.762 0.845 0.762 0.895 0.869
## [9591] 0.801 0.762 0.908 0.762 0.761 0.834 0.845 0.816 0.828 0.838
## [9601] 0.815 0.813 0.800 0.817 0.824 0.767 0.822 0.841 0.833 0.839
## [9611] 0.829 0.828 0.837 0.845 0.821 0.777 0.766 0.764 1.217 1.524
## [9621] 1.218 1.250 1.294 1.265 1.175 1.124 1.246 1.080 1.162 1.117
## [9631] 1.185 1.128 1.124 1.205 1.114 1.117 1.065 2.371 2.893 2.777
## [9641] 3.497 3.529 2.143 1.739 1.964 1.736 2.037 2.069 1.584 1.128
## [9651] 1.096 1.189 1.185 1.904 0.761 0.761 0.761 0.761 0.761 0.761
## [9661] 1.417 1.860 0.761 0.761 6.832 8.015 7.050 6.124 4.640 2.892
## [9671] 0.878 0.876 0.874 0.863 0.872 0.832 0.864 0.853 0.863 0.872
## [9681] 0.865 0.877 0.860 0.886 1.523 1.629 1.661 1.664 1.489 0.831
## [9691] 0.775 0.781 0.819 0.786 0.882 0.904 0.855 0.769 0.886 0.886
## [9701] 0.855 0.776 0.808 0.793 0.765 0.766 0.804 0.805 0.761 0.761
## [9711] 0.761 0.761 0.762 0.761 0.761 0.762 0.764 0.762 0.761 0.789
## [9721] 0.762 0.771 0.762 0.764 0.789 0.857 0.766 0.774 0.761 0.765
## [9731] 0.808 0.805 0.761 0.924 0.774 0.836 0.870 0.891 0.814 1.174
## [9741] 0.999 1.064 0.935 1.033 0.774 0.810 0.795 0.761 0.794 0.824
## [9751] 0.854 0.767 0.770 0.767 1.012 0.970 1.046 1.048 0.849 1.111
## [9761] 1.087 1.114 1.127 0.830 0.870 0.891 1.150 0.810 0.839 0.811
## [9771] 0.871 0.824 0.863 1.388 0.842 0.887 0.988 0.778 0.855 0.800
## [9781] 0.798 0.826 0.797 0.812 0.875 0.824 0.807 0.811 0.899 0.857
## [9791] 0.782 0.971 0.780 0.927 0.862 0.782 0.859 0.779 0.917 0.782
## [9801] 0.950 0.778 0.940 0.778 0.977 0.778 0.785 1.448 1.344 1.125
## [9811] 1.122 1.142 1.112 1.162 1.113 1.096 1.140 2.148 0.843 0.858
## [9821] 0.824 0.792 0.819 0.806 0.839 0.865 0.789 0.794 0.792 0.789
## [9831] 0.784 0.787 0.787 0.799 0.785 0.781 0.796 0.791 0.783 0.809
## [9841] 0.761 1.526 1.186 1.128 1.350 1.177 1.208 1.252 1.279 1.095
## [9851] 1.114 1.049 1.083 1.176 1.092 1.030 0.961 0.932 1.213 1.155
## [9861] 1.106 1.146 1.200 1.082 1.176 0.979 0.982 0.989 1.126 1.007
## [9871] 0.939 1.108 1.028 0.973 0.818 0.785 1.051 1.215 1.410 0.861
## [9881] 1.265 1.250 1.317 1.052 1.243 1.173 1.053 1.058 1.222 1.268
## [9891] 1.275 1.532 1.964 1.028 1.464 0.824 0.830 1.199 0.792 0.848
## [9901] 0.798 1.271 1.131 0.785 1.458 1.270 1.243 1.237 1.078 0.999
## [9911] 0.861 1.003 1.017 1.054 0.977 1.019 1.014 1.024 1.113 1.109
## [9921] 1.159 0.896 0.801 0.852 1.889 0.950 1.222 1.081 1.017 1.086
## [9931] 0.806 0.915 1.240 1.116 1.240 1.173 1.092 1.118 1.030 0.989
## [9941] 0.761 1.361 1.242 1.258 0.837 0.839 0.772 1.159 1.049 1.055
## [9951] 1.261 0.988 0.864 0.869 0.992 0.828 0.825 0.768 0.796 0.818
## [9961] 0.858 0.787 0.895 0.778 0.860 0.828 0.822 0.761 0.761 0.761
## [9971] 0.761 0.761 0.761 1.239 1.376 1.150 1.438 1.136 1.178 1.317
## [9981] 1.869 1.414 1.448 1.554 0.999 1.199 1.166 1.204 1.120 1.237
## [9991] 1.249 1.316 1.255 1.303 1.296 1.253 1.329 1.281 1.112 1.077
## [10001] 1.409 1.277 1.321 0.866 0.816 0.837 0.927 0.897 0.913 0.889
## [10011] 0.871 0.866 0.872 0.825 0.854 0.926 0.973 0.991 0.865 0.826
## [10021] 0.857 0.893 0.941 0.904 0.873 0.947 0.969 0.963 0.955 0.924
## [10031] 0.904 0.927 0.988 0.902 0.914 1.002 0.973 0.762 1.175 1.198
## [10041] 1.102 1.062 0.914 0.986 0.935 1.162 0.761 0.811 0.801 0.789
## [10051] 1.225 1.219 2.179 1.704 1.797 1.455 1.561 0.951 1.825 1.100
## [10061] 0.948 1.287 0.952 0.964 1.250 1.047 1.173 1.062 1.132 1.180
## [10071] 1.012 1.102 1.030 1.103 1.018 1.020 1.011 1.018 0.761 1.002
## [10081] 0.762 1.032 0.761 0.962 0.762 1.018 0.761 1.034 0.761 1.219
## [10091] 0.761 1.012 0.761 1.023 0.764 1.028 0.761 1.389 0.761 1.837
## [10101] 0.764 1.324 0.762 1.138 0.762 2.038 0.761 1.770 0.761 1.444
## [10111] 0.761 1.779 0.761 1.405 0.761 1.451 0.761 1.421 1.919 0.813
## [10121] 1.040 0.818 0.825 0.904 0.885 1.365 0.967 0.935 0.967 1.258
## [10131] 1.229 1.021 1.220 1.330 1.225 0.971 1.302 1.120 1.078 1.079
## [10141] 0.950 0.779 0.975 1.367 1.502 1.486 1.660 1.635 1.273 1.469
## [10151] 1.376 1.212 0.836 0.893 1.264 1.243 0.913 1.328 1.287 1.259
## [10161] 0.910 0.877 1.283 1.207 1.113 1.307 1.296 1.203 0.990 1.805
## [10171] 1.342 1.404 1.312 1.313 1.307 1.251 0.834 0.827 0.806 0.797
## [10181] 1.230 1.362 1.354 1.347 1.172 0.889 0.826 0.829 0.860 0.761
## [10191] 0.761 0.761 0.761 0.761 1.052 1.138 1.174 0.761 0.954 0.761
## [10201] 1.091 0.761 1.304 0.762 1.332 0.761 1.221 0.761 1.227 1.226
## [10211] 1.069 1.105 1.244 1.260 1.213 1.203 0.761 0.787 0.761 0.917
## [10221] 0.979 0.821 0.762 0.763 0.761 0.763 0.761 0.761 0.762 0.762
## [10231] 0.761 0.762 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761
## [10241] 0.761 0.761 0.761 0.761 0.771 0.896 1.170 0.983 0.943 0.923
## [10251] 0.775 0.872 1.093 0.937 0.765 1.131 1.015 1.139 0.789 0.949
## [10261] 0.966 0.890 0.977 0.788 0.845 0.838 0.775 0.842 0.799 0.866
## [10271] 1.224 0.861 1.121 0.761 0.813 0.816 0.800 0.772 0.779 0.818
## [10281] 0.833 0.792 0.815 0.781 0.797 0.793 0.795 0.812 0.794 0.881
## [10291] 0.802 0.941 0.802 0.861 0.813 0.883 0.805 0.874 0.795 0.838
## [10301] 0.846 0.850 0.785 0.844 0.798 0.831 0.824 0.847 0.810 0.965
## [10311] 0.847 0.778 0.778 0.775 0.821 0.786 0.767 0.780 0.777 0.767
## [10321] 0.763 0.766 0.772 0.761 0.778 0.812 0.776 0.769 0.773 0.779
## [10331] 0.783 0.809 0.796 0.824 0.799 0.800 0.792 0.813 0.810 1.019
## [10341] 1.093 1.270 1.301 1.196 1.106 1.164 1.044 1.105 1.090 1.301
## [10351] 2.025 1.243 1.420 1.322 1.484 1.428 1.365 1.372 1.153 2.226
## [10361] 1.458 1.259 1.416 1.473 1.637 1.473 1.103 1.278 1.342 0.897
## [10371] 1.229 1.146 0.857 1.270 1.418 1.738 1.216 1.363 1.037 1.272
## [10381] 1.286 1.402 1.827 1.428 1.750 1.723 1.581 1.604 1.604 1.894
## [10391] 1.995 1.423 1.451 1.255 1.147 1.276 1.275 1.369 1.504 1.368
## [10401] 1.308 1.401 1.615 1.083 1.528 0.988 1.191 1.636 1.570 1.120
## [10411] 0.803 0.787 0.769 0.765 0.766 0.776 0.792 0.761 0.767 0.848
## [10421] 0.761 1.016 0.764 1.134 0.864 1.127 0.890 1.061 0.826 0.986
## [10431] 1.685 1.785 0.776 1.272 1.350 1.338 0.876 1.118 0.868 0.767
## [10441] 0.783 0.786 0.780 0.795 0.855 0.849 0.802 0.868 0.885 0.817
## [10451] 0.815 0.775 0.762 0.761 1.882 1.703 1.134 1.005 1.445 1.317
## [10461] 1.073 0.995 1.266 1.359 1.179 1.147 1.151 1.149 1.229 1.498
## [10471] 1.287 1.052 1.025 0.897 1.121 1.203 1.252 1.171 1.236 1.142
## [10481] 1.338 1.094 0.889 0.996 1.031 0.851 0.972 1.057 1.310 1.269
## [10491] 0.992 1.095 1.192 1.143 1.203 1.113 1.190 0.899 1.145 1.183
## [10501] 1.249 1.258 1.115 1.043 1.013 1.102 1.172 1.156 0.992 0.989
## [10511] 0.765 0.841 0.928 0.767 0.806 1.429 0.765 0.765 0.762 1.493
## [10521] 0.761 0.761 0.765 0.762 0.762 0.762 0.765 0.761 0.761 0.944
## [10531] 0.761 0.765 0.765 1.641 2.219 1.550 0.975 0.797 0.785 0.989
## [10541] 0.878 0.906 1.651 1.645 1.966 1.408 0.815 0.769 0.769 0.769
## [10551] 0.765 0.774 0.766 0.870 1.534 0.885 0.765 0.780 0.789 0.812
## [10561] 0.856 0.912 0.841 0.799 0.893 0.925 1.018 0.960 0.972 1.124
## [10571] 1.021 0.977 1.094 0.839 0.917 0.853 0.788 0.861 1.023 1.180
## [10581] 1.063 1.260 1.137 1.321 1.315 1.150 0.924 0.864 0.839 0.876
## [10591] 0.913 1.017 0.922 1.050 1.425 1.082 0.928 1.006 0.866 1.098
## [10601] 1.070 1.051 1.116 1.028 1.080 1.202 0.863 1.057 1.046 1.774
## [10611] 1.581 2.157 2.379 2.570 1.447 1.583 1.186 1.055 1.326 1.479
## [10621] 1.294 1.280 0.940 0.998 1.038 1.049 1.053 0.972 0.763 0.763
## [10631] 0.763 0.763 0.763 0.761 0.762 0.761 0.763 0.763 0.763 0.763
## [10641] 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.901 0.761 0.830
## [10651] 0.761 0.950 0.761 0.980 0.763 1.051 0.763 1.608 0.761 1.870
## [10661] 0.761 2.792 0.761 1.755 0.761 1.198 0.761 1.422 0.762 1.572
## [10671] 0.761 1.519 0.761 1.188 0.761 1.050 0.761 1.063 0.761 0.980
## [10681] 0.762 1.695 0.761 1.311 0.761 1.598 0.761 0.763 0.761 0.761
## [10691] 0.761 0.761 0.761 0.763 0.761 0.761 0.761 0.761 0.761 0.761
## [10701] 0.761 0.761 0.761 0.763 0.761 0.761 0.761 0.763 0.763 0.763
## [10711] 0.998 1.213 1.565 2.601 2.785 2.281 1.741 2.324 2.323 1.992
## [10721] 1.904 2.449 1.937 3.336 0.761 0.931 1.191 1.260 0.965 1.101
## [10731] 0.874 1.063 1.069 0.868 1.179 1.137 1.197 0.915 1.016 1.087
## [10741] 1.055 1.715 1.477 1.058 1.227 1.099 0.997 1.712 2.001 1.234
## [10751] 1.228 1.255 1.363 1.442 1.400 1.667 1.858 1.729 1.949 2.110
## [10761] 1.735 1.604 1.691 1.707 1.642 1.365 1.700 1.678 0.764 0.761
## [10771] 0.761 1.539 1.157 0.761 1.116 0.798 1.035 0.873 0.913 0.880
## [10781] 0.761 0.761 0.761 0.929 1.014 0.761 1.082 1.039 0.978 1.055
## [10791] 1.075 1.830 1.072 1.085 0.978 1.054 0.971 0.944 0.901 0.986
## [10801] 1.002 1.002 1.051 1.066 1.068 1.379 1.352 1.011 0.761 0.873
## [10811] 0.761 0.761 0.790 1.331 1.467 1.218 2.316 1.800 0.989 1.565
## [10821] 1.328 1.732 1.615 1.373 1.619 1.503 1.270 1.800 1.705 1.491
## [10831] 1.080 0.994 1.002 1.130 0.762 0.761 0.761 0.833 0.804 1.279
## [10841] 1.112 1.035 1.106 1.241 1.232 2.087 2.668 2.327 1.754 1.709
## [10851] 1.430 1.938 1.247 1.903 2.341 1.692 1.778 1.609 1.493 1.445
## [10861] 1.974 1.294 1.178 0.761 0.761 0.761 0.761 0.762 1.089 0.915
## [10871] 1.176 1.159 1.143 3.370 1.154 1.119 1.108 1.034 1.193 1.169
## [10881] 1.191 1.140 1.293 1.196 1.977 0.964 2.504 3.531 1.736 1.425
## [10891] 1.281 1.271 1.960 1.385 1.280 1.433 2.544 1.486 1.857 1.516
## [10901] 1.946 1.397 1.577 1.600 1.731 1.236 1.503 1.758 1.345 1.403
## [10911] 2.263 1.596 2.161 1.299 1.622 1.398 1.833 0.951 1.694 1.439
## [10921] 1.950 0.773 1.871 1.296 1.645 0.878 2.165 2.428 2.134 1.255
## [10931] 1.378 1.362 1.370 1.319 1.404 1.283 1.275 1.921 1.764 1.422
## [10941] 0.768 0.761 0.818 0.761 0.964 0.894 0.773 0.854 0.957 0.761
## [10951] 0.761 0.899 2.089 0.965 1.390 0.992 1.018 1.009 1.008 1.040
## [10961] 1.169 0.989 1.307 0.991 1.398 0.914 1.034 1.030 1.000 1.285
## [10971] 1.259 1.310 1.277 1.186 1.170 1.300 1.190 1.164 1.517 0.984
## [10981] 0.986 0.925 0.879 0.866 0.906 0.823 0.982 0.779 1.049 0.801
## [10991] 0.783 1.148 1.081 1.145 0.800 1.416 0.761 1.160 1.159 0.992
## [11001] 0.965 0.765 0.779 0.763 0.798 1.071 1.077 2.215 1.558 1.902
## [11011] 2.691 1.651 1.862 2.102 1.783 1.780 1.768 2.278 2.009 1.779
## [11021] 1.898 1.662 1.673 1.808 2.003 1.925 1.760 2.487 2.063 0.933
## [11031] 1.466 1.186 1.134 1.133 0.978 1.446 1.193 1.657 1.402 1.082
## [11041] 1.276 1.058 1.026 1.036 1.159 1.227 0.958 1.054 1.183 1.177
## [11051] 1.241 1.027 0.837 0.949 0.920 0.911 1.037 0.938 0.963 0.924
## [11061] 1.065 1.109 1.087 1.129 1.135 1.076 1.050 0.920 0.934 0.998
## [11071] 1.006 1.063 0.875 0.965 0.980 0.843 1.036 0.907 1.284 0.761
## [11081] 1.451 1.386 1.316 1.193 0.987 1.148 1.029 1.089 1.366 1.531
## [11091] 1.029 1.425 1.048 1.072 0.818 0.761 1.042 1.089 1.105 0.914
## [11101] 1.048 1.106 1.055 0.995 1.044 0.990 1.043 1.066 0.761 0.770
## [11111] 0.826 0.817 0.814 0.793 0.898 1.343 0.852 0.818 1.573 1.574
## [11121] 0.985 1.003 0.761 1.554 0.845 0.939 1.103 1.553 1.658 1.038
## [11131] 1.675 0.845 1.086 0.999 1.294 1.414 0.896 1.503 0.917 1.430
## [11141] 1.546 1.141 1.507 1.466 1.175 0.947 0.761 0.761 1.339 0.881
## [11151] 0.908 1.619 1.882 0.887 0.810 1.218 1.188 1.011 0.953 1.030
## [11161] 1.140 1.074 1.033 0.794 1.169 0.855 0.912 0.905 0.824 0.796
## [11171] 0.856 0.852 0.802 0.786 0.784 0.817 0.833 0.761 0.761 0.958
## [11181] 1.004 1.145 0.765 0.790 0.945 1.144 0.765 0.765 0.762 0.765
## [11191] 1.557 1.243 1.404 1.339 0.762 0.822 0.946 1.224 1.140 1.135
## [11201] 1.235 0.823 1.486 1.229 1.302 1.443 1.791 2.525 1.741 2.043
## [11211] 1.317 1.255 1.251 2.190 0.981 1.362 1.389 1.386 1.232 0.765
## [11221] 0.763 0.766 0.765 0.763 0.763 0.763 0.784 0.891 0.897 1.300
## [11231] 0.962 0.945 1.087 0.924 1.045 1.044 1.003 0.871 0.918 0.895
## [11241] 0.864 0.848 0.974 0.855 0.762 0.762 0.762 0.762 0.761 0.816
## [11251] 0.761 0.768 0.762 0.762 0.762 0.779 0.765 2.671 0.762 2.155
## [11261] 0.762 1.663 0.766 1.628 0.761 1.555 1.612 1.739 1.623 1.703
## [11271] 1.712 1.164 1.150 1.403 1.347 0.762 1.428 0.761 0.762 0.761
## [11281] 0.761 0.761 0.762 0.762 0.761 0.762 0.762 0.762 0.940 1.543
## [11291] 1.549 0.775 0.871 1.628 0.789 0.810 0.765 0.762 0.765 0.761
## [11301] 0.976 0.880 0.867 0.857 0.761 0.785 0.794 0.925 1.284 1.200
## [11311] 1.315 0.785 1.675 1.396 1.580 0.835 0.851 0.813 0.805 0.795
## [11321] 0.761 0.763 1.542 1.569 1.518 0.833 0.792 0.848 0.770 1.602
## [11331] 1.591 0.834 1.415 0.781 0.805 1.521 1.604 0.815 1.587 0.851
## [11341] 1.368 1.510 0.927 1.633 0.790 0.868 0.828 0.809 0.771 0.800
## [11351] 0.776 0.861 0.853 0.977 0.901 0.799 0.860 0.827 0.879 0.815
## [11361] 0.879 0.827 0.925 0.821 0.852 0.837 0.795 0.846 0.879 0.853
## [11371] 0.877 0.862 0.767 0.781 0.765 0.769 0.798 0.779 0.791 0.855
## [11381] 0.953 0.830 0.765 0.762 0.767 0.765 0.771 0.765 0.763 0.770
## [11391] 0.773 0.766 0.766 0.767 0.763 0.762 0.764 0.763 0.769 0.765
## [11401] 0.767 0.768 0.768 0.771 0.775 0.770 0.805 0.807 0.761 0.761
## [11411] 0.866 0.769 0.801 0.782 0.763 0.765 0.763 0.771 0.768 0.779
## [11421] 0.770 0.816 0.816 0.771 0.779 0.793 0.802 0.815 0.771 0.768
## [11431] 0.776 0.770 0.783 0.803 1.051 0.856 0.795 0.788 0.930 1.288
## [11441] 0.761 0.760 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [11451] 0.761 0.787 0.761 0.870 0.761 0.761 0.761 0.761 0.761 1.048
## [11461] 1.480 2.654 1.225 0.884 0.824 1.002 0.955 0.784 0.922 0.936
## [11471] 0.881 1.396 0.908 0.963 1.158 1.033 0.940 0.945 0.789 0.863
## [11481] 0.847 0.794 0.828 0.797 0.782 0.824 1.502 2.009 1.254 1.455
## [11491] 1.512 1.172 2.251 1.504 0.793 0.764 0.796 0.762 0.761 0.776
## [11501] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [11511] 0.761 0.761 0.761 0.824 0.775 0.793 0.807 0.783 0.774 0.783
## [11521] 0.764 0.819 0.766 0.766 0.811 0.799 0.762 0.808 0.766 0.775
## [11531] 0.763 0.762 0.857 0.762 0.761 0.763 0.769 0.766 0.762 1.059
## [11541] 0.828 0.891 0.943 0.861 0.869 1.290 1.971 0.828 0.783 0.764
## [11551] 0.778 0.806 0.833 0.830 0.867 0.875 0.835 0.815 0.867 0.870
## [11561] 0.984 0.842 1.093 0.999 0.935 0.829 0.896 0.955 1.480 3.972
## [11571] 0.761 0.761 0.761 0.982 0.761 0.866 0.907 0.954 0.931 0.898
## [11581] 0.899 1.119 0.877 0.825 0.956 0.781 0.803 0.802 0.777 0.890
## [11591] 0.783 0.960 0.762 0.761 0.761 0.761 1.959 1.246 1.686 1.147
## [11601] 1.444 1.291 1.252 1.302 0.874 0.836 0.896 0.806 0.778 0.782
## [11611] 0.774 0.779 1.029 1.133 1.098 1.243 1.305 1.159 1.270 1.115
## [11621] 1.682 1.453 0.762 0.797 0.764 0.771 0.851 0.927 0.904 1.462
## [11631] 1.910 1.935 0.761 0.889 1.071 0.892 1.110 0.943 0.761 0.762
## [11641] 0.761 0.761 0.761 0.761 1.293 1.521 1.285 1.340 1.341 1.335
## [11651] 1.726 1.315 0.761 1.319 1.405 1.612 1.191 1.177 1.291 1.327
## [11661] 1.180 1.297 1.182 1.132 1.379 1.363 1.372 1.237 1.487 1.361
## [11671] 1.348 0.761 0.761 0.761 0.761 0.875 0.931 1.315 1.131 1.130
## [11681] 1.145 0.777 4.348 0.956 1.035 0.832 0.983 2.294 1.209 1.706
## [11691] 2.326 0.933 1.284 1.367 2.714 1.410 2.672 1.435 1.113 1.812
## [11701] 1.614 0.762 0.768 0.768 0.765 0.841 0.879 0.890 0.799 0.766
## [11711] 0.820 0.769 0.781 0.794 0.771 0.763 0.774 0.777 0.783 0.777
## [11721] 0.773 0.793 0.799 0.782 0.781 0.786 0.774 0.772 0.848 0.977
## [11731] 0.847 0.927 0.762 0.994 0.762 0.761 0.853 0.761 0.775 0.763
## [11741] 0.916 0.851 1.599 1.551 1.084 0.787 0.832 0.769 0.928 0.768
## [11751] 2.258 2.491 0.761 0.761 1.158 0.904 0.763 0.766 0.959 1.584
## [11761] 0.974 1.073 1.082 0.762 0.761 0.891 1.027 1.132 1.169 0.761
## [11771] 0.761 1.027 0.761 1.208 1.199 1.160 1.170 1.021 0.761 0.761
## [11781] 1.012 0.899 0.891 1.007 0.977 0.817 0.884 0.867 0.947 0.775
## [11791] 0.823 0.794 0.863 0.912 0.788 0.831 0.784 0.775 0.791 0.925
## [11801] 1.390 1.202 0.818 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [11811] 0.761 0.841 0.761 0.761 0.761 0.954 0.761 0.761 0.761 0.761
## [11821] 1.169 1.158 1.341 1.279 1.752 1.414 4.375 2.072 1.672 1.676
## [11831] 0.925 0.828 0.857 0.936 0.795 0.834 0.826 0.789 0.887 0.792
## [11841] 0.788 0.889 0.918 1.068 1.104 0.933 0.761 0.761 0.869 0.761
## [11851] 0.786 0.832 0.891 0.834 0.869 0.852 0.867 1.229 0.840 0.970
## [11861] 1.280 0.822 1.438 0.762 0.762 0.767 0.770 0.762 0.763 0.777
## [11871] 0.765 0.787 0.769 0.770 0.762 0.763 0.802 0.775 0.780 0.899
## [11881] 1.193 1.507 1.087 0.762 0.769 0.761 0.765 0.789 0.761 0.762
## [11891] 0.816 0.919 0.904 0.926 0.979 0.915 1.020 1.061 0.888 0.822
## [11901] 1.050 0.817 1.077 0.983 2.416 1.644 0.962 0.934 0.881 0.889
## [11911] 0.935 1.056 0.937 0.898 0.869 0.875 0.868 1.736 0.778 0.828
## [11921] 0.932 0.854 0.851 0.960 0.844 0.980 1.033 1.286 1.025 1.380
## [11931] 0.982 0.989 0.949 0.849 0.900 1.031 1.015 0.856 0.984 2.309
## [11941] 0.901 1.059 1.136 4.256 2.977 0.978 0.795 1.020 0.989 1.041
## [11951] 1.159 1.155 0.790 1.207 1.277 1.429 1.399 0.892 1.212 0.852
## [11961] 1.065 0.905 0.860 0.763 0.762 0.787 0.788 0.788 0.790 0.793
## [11971] 0.761 0.822 0.808 0.788 0.783 0.775 0.766 0.792 0.765 0.778
## [11981] 0.767 0.780 0.784 0.800 0.790 0.935 0.853 0.810 0.796 0.819
## [11991] 0.779 0.789 1.116 0.893 0.867 0.829 1.008 1.012 1.229 1.221
## [12001] 0.999 1.103 0.799 0.773 0.779 0.797 0.801 0.882 0.901 0.841
## [12011] 1.044 1.035 0.913 0.905 0.879 1.024 1.074 2.863 3.173 2.860
## [12021] 2.710 0.835 0.852 1.227 0.859 1.234 0.821 0.826 2.447 1.498
## [12031] 0.761 0.817 0.761 1.939 1.277 1.324 0.762 0.799 0.792 1.006
## [12041] 0.761 0.828 0.853 0.761 0.776 1.049 1.063 2.774 2.038 0.762
## [12051] 0.933 0.968 0.914 0.762 0.762 0.911 1.448 1.097 0.776 0.774
## [12061] 0.770 0.768 0.775 0.772 0.772 0.773 0.769 0.768 0.765 0.764
## [12071] 0.765 0.764 0.764 0.763 0.764 0.763 0.763 0.764 0.764 0.789
## [12081] 0.779 0.782 0.767 0.777 0.786 1.067 1.096 0.994 1.096 1.119
## [12091] 1.279 1.320 1.054 1.130 1.213 1.227 1.027 0.876 1.169 1.112
## [12101] 1.017 0.941 0.916 0.816 0.766 0.762 0.843 1.130 0.838 0.870
## [12111] 0.858 0.892 0.811 1.926 0.761 1.945 1.988 1.633 1.496 1.375
## [12121] 1.524 1.410 1.524 1.474 1.410 2.082 1.507 2.113 2.174 1.551
## [12131] 1.431 1.810 1.412 1.124 1.097 1.287 0.761 0.761 0.761 0.761
## [12141] 0.761 0.761 0.761 1.120 0.761 0.762 0.774 0.774 0.798 0.932
## [12151] 1.232 1.475 1.454 0.947 0.985 0.841 0.838 1.171 1.229 1.370
## [12161] 0.763 0.762 0.762 0.762 0.762 0.763 0.764 0.762 0.763 0.762
## [12171] 0.762 0.762 0.764 0.766 0.764 0.765 0.765 0.763 0.763 0.769
## [12181] 0.765 0.766 0.770 0.769 0.763 0.766 0.769 0.766 0.761 0.762
## [12191] 0.761 0.762 0.761 0.761 0.764 0.765 0.762 0.857 0.764 0.762
## [12201] 0.800 0.767 0.795 0.830 0.767 0.792 0.761 0.793 0.762 0.762
## [12211] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.770 0.767 0.911
## [12221] 0.815 0.907 0.792 0.857 0.762 0.807 0.771 0.770 0.763 0.785
## [12231] 0.769 0.845 0.789 0.810 0.883 0.836 0.860 0.800 0.764 0.774
## [12241] 0.781 0.768 0.763 0.956 0.813 0.846 0.827 0.784 1.048 1.142
## [12251] 1.159 0.875 0.936 0.993 0.809 0.785 0.793 0.804 0.783 0.807
## [12261] 0.858 0.822 0.809 0.856 0.922 0.941 0.836 0.970 1.009 0.890
## [12271] 0.882 0.795 0.761 0.762 0.761 0.798 0.761 0.761 0.761 0.761
## [12281] 0.761 0.761 0.761 0.761 0.761 0.881 0.761 0.764 0.768 0.766
## [12291] 0.763 0.761 0.761 0.768 0.767 0.767 0.778 0.772 0.771 0.764
## [12301] 0.765 0.776 0.766 0.762 0.761 1.601 1.415 1.111 1.231 1.371
## [12311] 3.307 2.694 1.931 1.930 0.944 0.803 0.787 0.783 0.767 0.767
## [12321] 2.112 0.816 0.950 0.761 0.998 0.762 0.774 0.763 1.515 1.567
## [12331] 0.826 1.359 1.453 0.857 1.411 1.625 1.600 1.391 0.873 1.374
## [12341] 1.874 1.684 1.581 1.239 1.140 1.173 1.355 1.095 0.761 0.871
## [12351] 0.934 1.050 1.098 1.048 1.060 0.761 0.761 0.761 0.828 0.835
## [12361] 0.975 1.064 0.761 0.781 0.761 0.802 0.801 0.900 0.894 0.867
## [12371] 0.891 0.785 1.283 2.674 1.047 1.007 1.062 1.217 1.164 2.288
## [12381] 0.761 0.761 0.762 0.769 0.766 0.878 0.957 1.034 0.834 0.826
## [12391] 0.930 0.761 0.762 0.786 0.762 0.813 0.818 0.762 0.773 0.762
## [12401] 0.817 0.936 0.958 0.874 0.822 0.793 0.789 0.886 0.918 3.084
## [12411] 4.436 0.899 0.799 0.766 0.772 1.051 1.124 0.827 1.044 0.958
## [12421] 0.794 0.790 0.838 0.838 0.796 0.827 0.840 0.837 0.790 0.844
## [12431] 0.765 0.762 0.761 0.761 0.761 0.761 0.763 0.762 0.761 0.761
## [12441] 0.761 0.767 0.792 0.761 0.761 0.761 0.761 0.762 0.762 0.761
## [12451] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.908 0.761
## [12461] 0.761 1.439 1.219 1.494 0.763 1.411 0.763 0.763 0.892 0.846
## [12471] 1.121 1.090 1.449 1.199 1.029 1.178 0.763 0.762 0.764 0.766
## [12481] 0.765 0.764 0.764 0.763 0.764 0.761 0.761 0.761 0.761 0.761
## [12491] 0.761 0.762 0.765 0.762 0.761 0.761 0.762 0.762 0.761 0.762
## [12501] 0.764 0.763 0.761 0.816 2.833 4.152 2.977 2.133 0.761 0.761
## [12511] 0.761 0.761 1.278 1.213 1.262 0.761 0.860 1.234 1.316 0.869
## [12521] 0.869 0.805 0.791 1.261 1.298 1.112 0.764 0.764 0.873 0.766
## [12531] 0.763 0.873 1.106 1.173 1.105 0.842 1.149 0.992 0.867 0.818
## [12541] 1.100 0.830 0.798 0.813 0.804 0.829 0.917 0.826 0.825 0.818
## [12551] 1.202 0.828 0.808 0.809 0.823 0.852 0.805 0.800 0.787 0.804
## [12561] 0.814 0.785 0.798 0.804 0.787 0.809 0.816 0.824 0.802 0.761
## [12571] 1.036 0.966 0.955 1.047 1.034 1.009 1.032 0.762 0.762 0.762
## [12581] 0.762 1.121 0.764 0.766 0.763 0.816 0.761 0.761 0.761 0.761
## [12591] 0.761 0.791 0.863 0.839 0.842 0.834 0.814 0.847 0.797 0.802
## [12601] 0.767 0.858 0.802 0.824 0.812 0.856 0.813 0.808 0.789 0.800
## [12611] 0.777 0.771 0.773 0.779 0.786 0.781 0.795 0.797 0.789 0.811
## [12621] 0.894 0.762 0.784 0.781 0.784 0.807 0.784 0.762 0.789 0.762
## [12631] 1.120 0.815 0.801 0.795 0.792 0.792 0.797 0.791 0.789 0.782
## [12641] 0.794 0.802 0.818 0.802 0.808 0.837 1.045 0.785 0.795 0.790
## [12651] 0.790 0.787 1.174 0.866 1.159 0.858 0.836 1.177 0.797 1.009
## [12661] 0.831 1.186 1.098 1.002 0.824 0.856 0.835 0.864 0.878 0.889
## [12671] 0.850 0.808 0.996 0.777 0.788 0.778 0.792 0.793 0.787 0.787
## [12681] 0.783 0.771 0.784 0.776 0.828 0.792 0.781 0.805 0.828 0.805
## [12691] 0.805 0.773 1.087 0.779 1.075 0.773 0.915 0.770 0.960 0.799
## [12701] 1.032 0.802 0.948 0.805 0.899 0.799 0.835 0.788 0.835 0.829
## [12711] 0.821 0.817 0.805 0.826 0.831 0.911 0.806 0.807 0.792 0.784
## [12721] 0.783 1.067 0.761 0.761 0.761 0.761 0.809 0.801 0.761 0.762
## [12731] 0.835 0.789 0.761 0.786 1.019 0.780 0.787 0.799 0.819 0.819
## [12741] 0.819 0.798 0.782 0.778 0.782 0.791 0.780 0.788 0.788 0.791
## [12751] 0.777 0.776 0.776 1.052 0.761 0.761 0.761 0.762 0.793 0.766
## [12761] 0.813 0.840 1.095 1.165 0.838 1.071 0.823 0.974 0.983 0.904
## [12771] 0.812 0.822 0.798 0.843 0.805 0.784 0.789 0.797 0.793 0.795
## [12781] 0.782 0.787 0.783 0.783 0.786 0.781 0.773 0.796 0.792 0.863
## [12791] 0.843 0.848 0.812 0.826 0.838 0.816 0.800 0.816 0.817 0.804
## [12801] 0.823 0.818 0.870 0.845 0.853 0.829 0.797 0.784 1.093 0.764
## [12811] 0.761 0.775 0.764 0.811 0.813 0.793 0.833 0.846 0.828 0.823
## [12821] 0.849 0.853 0.812 0.808 0.849 0.838 0.841 0.841 0.849 0.858
## [12831] 0.858 0.849 0.860 0.767 0.808 1.056 0.761 0.761 0.773 0.784
## [12841] 0.807 0.808 0.795 0.794 0.835 0.796 0.817 0.849 0.832 1.148
## [12851] 0.816 1.061 1.103 1.106 1.117 0.815 1.088 0.779 1.079 1.193
## [12861] 0.848 1.014 0.788 0.800 0.803 0.787 0.779 0.843 0.797 0.799
## [12871] 0.809 0.891 0.844 0.812 0.794 0.814 0.818 0.886 0.788 0.830
## [12881] 0.793 0.803 0.780 0.774 0.768 0.798 0.793 0.776 0.816 0.790
## [12891] 0.795 0.778 0.797 0.763 0.799 0.792 1.074 0.951 0.895 1.148
## [12901] 1.098 1.139 1.176 1.272 1.183 1.221 0.836 0.780 0.788 0.766
## [12911] 0.831 0.798 0.822 0.951 0.842 0.817 0.817 1.182 0.799 0.816
## [12921] 0.893 0.807 0.799 0.815 0.912 0.956 1.014 0.845 0.930 0.955
## [12931] 0.920 0.835 0.828 0.812 0.821 0.854 0.922 0.871 0.874 0.849
## [12941] 0.900 0.846 0.830 0.859 0.823 0.861 0.843 0.816 0.834 0.829
## [12951] 0.789 0.762 0.764 0.774 0.764 0.764 0.763 0.839 0.781 0.785
## [12961] 0.789 0.791 0.789 0.784 0.776 0.776 0.772 0.796 0.787 0.792
## [12971] 0.787 0.799 0.774 0.779 0.768 0.762 0.763 0.767 0.763 0.762
## [12981] 0.762 0.761 0.766 0.763 0.825 0.862 0.887 0.903 0.857 0.865
## [12991] 0.872 0.803 0.810 0.806 0.833 0.810 0.808 0.814 0.823 0.836
## [13001] 0.877 0.827 0.785 0.788 0.804 0.800 0.797 0.772 0.812 0.819
## [13011] 0.831 0.814 0.836 0.980 0.823 0.994 0.936 0.908 0.985 0.942
## [13021] 0.919 0.888 0.873 0.909 0.887 0.876 0.910 0.828 0.811 0.829
## [13031] 0.864 0.812 0.889 0.832 0.864 0.823 0.895 0.901 0.853 0.821
## [13041] 0.807 0.778 0.789 0.794 0.784 0.782 0.791 0.889 0.873 0.845
## [13051] 0.837 0.854 0.816 0.899 0.807 0.815 0.841 0.857 0.829 0.815
## [13061] 0.845 0.851 0.862 0.856 0.841 0.776 0.810 0.771 0.789 0.780
## [13071] 0.804 0.787 0.803 0.788 1.049 0.833 0.920 0.825 0.823 0.848
## [13081] 0.792 0.885 0.940 0.885 0.931 0.924 0.895 0.880 0.867 0.853
## [13091] 0.857 0.829 0.852 0.852 0.826 0.934 0.895 0.883 0.892 0.819
## [13101] 0.861 0.805 0.879 0.819 0.775 0.809 0.856 0.875 0.845 0.817
## [13111] 0.807 0.821 0.834 0.825 0.879 0.835 0.827 0.877 0.856 0.840
## [13121] 0.813 0.827 0.819 0.821 0.804 0.814 0.836 0.816 0.816 0.833
## [13131] 0.840 0.808 0.856 0.894 0.836 0.880 0.845 0.821 0.875 0.830
## [13141] 0.768 0.783 0.785 0.776 0.778 0.803 0.785 0.800 0.807 0.801
## [13151] 0.792 0.787 0.832 0.792 0.789 0.786 0.784 0.791 0.792 0.795
## [13161] 0.791 0.811 0.781 0.772 0.774 0.777 0.785 0.798 0.779 0.786
## [13171] 0.766 0.779 0.777 0.765 0.774 0.775 0.777 0.781 0.802 0.816
## [13181] 0.808 0.802 0.808 0.801 0.804 0.801 0.787 0.786 0.787 0.779
## [13191] 0.787 0.796 0.787 0.793 0.794 0.795 0.780 0.769 0.770 0.770
## [13201] 0.772 0.774 0.774 0.771 0.781 0.775 0.973 0.784 0.791 0.788
## [13211] 0.811 0.836 0.786 0.957 0.825 0.822 0.869 0.819 0.821 0.808
## [13221] 0.797 0.807 0.791 0.790 0.895 0.820 0.818 0.815 0.873 0.876
## [13231] 0.788 0.857 0.831 0.790 0.848 0.822 0.794 0.801 0.815 0.782
## [13241] 0.799 0.795 0.802 0.808 0.798 0.822 0.810 0.808 0.811 0.795
## [13251] 0.804 0.800 0.794 0.804 0.804 0.799 0.792 0.803 0.805 0.776
## [13261] 0.784 0.791 0.815 0.826 0.799 0.808 0.819 0.808 0.817 0.784
## [13271] 0.805 0.806 0.863 0.818 0.828 0.800 0.829 0.853 0.787 1.026
## [13281] 0.930 0.927 0.907 0.875 0.836 0.835 0.815 0.826 0.785 0.784
## [13291] 0.807 0.806 0.830 0.828 0.841 0.804 0.829 0.856 0.858 0.805
## [13301] 0.843 0.813 0.789 0.798 0.817 0.797 0.801 0.798 0.796 0.802
## [13311] 0.894 0.923 0.877 0.824 0.859 0.819 0.847 0.858 0.807 0.788
## [13321] 0.785 0.806 0.774 0.785 0.805 0.789 0.802 0.800 0.778 0.785
## [13331] 0.802 0.780 0.796 0.817 0.851 0.776 0.779 0.779 0.765 0.772
## [13341] 0.774 0.772 0.777 0.813 0.825 0.806 0.799 0.799 0.807 0.814
## [13351] 0.812 0.801 0.827 0.813 0.797 0.806 0.826 0.817 0.796 0.808
## [13361] 0.821 0.806 0.773 0.774 0.776 0.786 0.771 0.776 0.765 0.774
## [13371] 0.772 0.885 0.913 0.871 0.964 0.929 0.954 0.939 0.848 0.855
## [13381] 0.861 0.852 0.811 0.834 0.861 0.841 0.863 0.861 0.799 0.776
## [13391] 0.815 0.822 0.802 0.777 0.774 0.766 0.767 0.772 0.770 0.776
## [13401] 0.797 0.843 0.813 0.845 0.826 0.816 0.844 0.805 0.798 0.775
## [13411] 0.787 0.780 0.775 0.775 0.774 0.792 0.777 0.781 0.770 0.774
## [13421] 0.773 0.774 0.766 0.771 0.765 0.765 0.770 0.769 0.786 0.892
## [13431] 0.787 0.804 0.835 0.811 0.792 0.798 0.785 0.790 0.782 0.780
## [13441] 0.825 0.787 0.782 0.793 0.836 0.863 0.823 0.787 0.806 0.834
## [13451] 0.807 0.875 0.818 0.855 0.808 0.802 0.822 0.816 0.834 0.829
## [13461] 0.820 0.807 0.810 0.804 0.807 0.812 0.847 0.798 0.805 0.799
## [13471] 0.808 0.796 0.798 0.805 0.794 0.795 0.803 0.793 0.773 0.795
## [13481] 0.802 0.813 0.828 0.843 0.814 0.871 0.886 0.787 0.949 0.934
## [13491] 0.909 0.933 0.932 0.900 0.895 0.841 0.876 0.863 0.900 0.889
## [13501] 0.925 0.878 0.905 0.949 0.880 0.858 0.864 0.860 0.837 0.859
## [13511] 0.834 0.811 0.825 0.837 1.087 0.802 0.828 0.816 0.859 0.824
## [13521] 0.834 0.912 0.884 0.878 0.932 0.981 0.861 0.823 0.805 0.826
## [13531] 0.796 0.818 0.835 0.831 0.833 0.863 0.901 0.907 0.861 0.888
## [13541] 0.857 0.843 0.830 0.819 0.940 0.865 0.848 0.888 0.864 0.924
## [13551] 0.921 0.909 0.847 0.828 0.954 0.940 0.884 0.917 0.942 0.883
## [13561] 0.891 0.826 0.846 0.815 0.841 0.852 0.850 0.855 0.851 0.852
## [13571] 0.867 0.830 0.910 0.829 0.867 0.823 0.830 0.882 0.922 0.833
## [13581] 1.082 0.827 0.900 0.853 0.877 0.817 0.781 0.930 0.832 0.918
## [13591] 0.871 0.857 0.878 0.816 0.825 0.826 0.828 0.844 0.848 0.845
## [13601] 0.866 0.851 0.884 0.859 0.826 0.858 0.884 0.811 0.912 0.829
## [13611] 0.814 0.861 0.870 0.959 0.886 0.981 0.975 0.931 0.982 0.804
## [13621] 0.903 0.812 0.825 0.828 0.976 0.952 0.965 0.979 0.926 0.979
## [13631] 0.974 1.010 0.987 0.973 0.957 0.939 0.869 0.862 0.913 0.930
## [13641] 0.956 0.898 0.920 0.808 0.934 0.925 0.812 0.886 0.871 0.893
## [13651] 0.864 0.924 0.854 0.891 0.860 0.860 0.856 0.875 0.872 0.813
## [13661] 0.827 0.824 0.838 0.840 0.840 0.834 0.828 0.804 0.817 0.836
## [13671] 0.834 0.854 0.829 0.867 0.852 0.873 0.850 0.869 0.850 0.768
## [13681] 0.836 0.780 0.805 0.827 0.909 0.889 0.861 0.891 0.886 0.885
## [13691] 0.835 0.796 0.799 0.854 0.820 0.821 0.838 0.830 0.835 0.840
## [13701] 0.868 0.825 0.797 0.822 0.800 0.791 0.775 0.789 0.764 0.795
## [13711] 0.804 4.327 5.747 5.744 6.663 4.367 4.265 3.975 3.942 4.179
## [13721] 3.183 3.926 3.933 3.301 3.645 3.479 4.090 3.835 4.712 4.228
## [13731] 4.228 1.839 4.087 3.290 2.275 1.971 1.515 2.487 4.031 0.762
## [13741] 1.212 1.532 0.762 1.458 0.762 1.641 1.496 1.553 2.499 2.023
## [13751] 2.120 0.930 0.764 1.571 1.870 1.846 1.396 1.920 1.792 2.482
## [13761] 2.161 2.737 2.806 2.750 1.518 0.762 2.399 1.360 0.878 0.822
## [13771] 0.825 0.843 0.829 0.819 0.792 0.805 0.802 0.786 0.825 0.816
## [13781] 0.798 0.915 1.331 1.076 0.881 1.143 0.765 0.761 0.762 0.762
## [13791] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [13801] 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761 0.761
## [13811] 0.761 0.761 0.761 0.761 0.761 0.763 0.763 0.760 0.815 0.999
## [13821] 0.781 0.768 0.761 1.421 1.175 1.180 1.527 0.761 0.761 1.648
## [13831] 0.973 1.190 0.762 1.545 0.762 1.973 1.066 2.005 0.844 2.066
## [13841] 0.762 2.205 0.762 1.783 0.762 1.833 0.762 1.058 0.762 0.761
## [13851] 0.841 1.646 1.387 1.808 1.365 1.840 1.656 2.108 1.221 1.788
## [13861] 1.312 1.525 0.868 0.970 0.895 0.760 0.824 0.834 0.873 1.196
## [13871] 0.867 1.157 0.989 1.950 1.113 2.569 2.296 1.503 1.123 1.458
## [13881] 0.761 1.243 1.243 1.233 1.767 1.318 1.730 1.434 1.390 1.504
## [13891] 1.269 1.705 1.352 1.506 1.799 1.729 1.531 1.673 1.266 1.027
## [13901] 0.947 0.840 1.216 1.147 1.005 1.124 1.075 1.066 1.030 0.779
## [13911] 0.777 0.781 0.779 0.790 0.793 0.785 0.775 0.772 0.779 0.765
## [13921] 0.767 0.780 0.773 0.766 0.976 0.761 0.761 0.761 0.761 0.761
## [13931] 0.762 0.761 0.762 0.762 0.762 0.760 0.761 0.966 0.760 0.766
## [13941] 0.762 0.808 0.760 0.792 0.760 0.762 0.760 0.767 0.760 0.764
## [13951] 0.760 0.760 0.760 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [13961] 0.761 0.761 0.761 0.761 0.761 0.761 0.941 0.773 0.776 0.761
## [13971] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762
## [13981] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [13991] 0.761 0.761 0.762 0.761 0.761 0.822 0.762 0.762 0.761 0.761
## [14001] 0.761 0.761 0.761 1.366 0.762 0.762 0.761 0.761 0.761 0.761
## [14011] 0.761 0.761 1.595 0.875 0.761 0.761 0.766 1.541 0.762 0.762
## [14021] 0.762 0.762 0.762 0.762 3.043 1.229 1.741 0.859 1.955 0.762
## [14031] 0.762 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.762 0.762
## [14041] 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.769 0.761 0.761
## [14051] 0.761 0.761 0.798 0.811 0.761 0.761 0.839 0.812 0.762 0.798
## [14061] 0.762 0.800 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [14071] 0.762 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.762 0.762
## [14081] 0.761 0.761 0.761 0.761 0.761 1.519 1.043 0.837 0.761 0.761
## [14091] 0.880 0.761 1.541 2.276 1.464 1.816 1.739 1.571 1.983 1.227
## [14101] 0.762 1.940 0.762 1.594 1.843 1.756 0.971 0.909 0.774 0.761
## [14111] 0.769 0.761 0.777 0.761 0.776 0.761 0.772 0.761 0.769 0.762
## [14121] 0.773 0.777 0.797 0.781 0.761 0.787 0.761 0.813 0.761 0.782
## [14131] 0.764 0.792 0.970 0.761 0.761 0.761 0.761 0.761 0.761 0.760
## [14141] 0.761 0.762 0.816 0.762 0.762 0.762 0.762 0.761 0.762 0.761
## [14151] 0.761 0.762 0.761 0.761 0.761 0.920 0.761 0.762 0.775 0.761
## [14161] 0.789 0.921 0.943 0.957 0.966 0.944 0.829 0.939 0.902 0.952
## [14171] 0.900 0.943 0.953 0.949 0.908 0.908 0.761 0.761 0.761 0.761
## [14181] 0.761 0.762 0.761 0.762 0.762 0.762 0.761 0.957 0.941 0.946
## [14191] 0.875 0.942 1.034 1.953 2.180 1.072 1.245 0.847 3.274 1.189
## [14201] 1.149 1.319 0.778 0.799 0.782 0.779 0.784 0.774 0.790 0.761
## [14211] 0.784 0.761 0.800 1.221 0.794 0.761 0.862 0.868 0.845 0.847
## [14221] 0.811 0.957 0.915 0.773 0.924 0.761 0.839 0.835 0.761 0.761
## [14231] 0.799 0.818 0.813 0.805 0.827 0.784 0.799 0.803 0.842 0.790
## [14241] 0.782 0.789 0.761 0.762 0.771 0.789 0.776 1.265 0.777 0.934
## [14251] 0.783 0.761 0.763 0.761 0.771 0.761 0.806 2.332 0.797 1.479
## [14261] 0.812 1.464 0.771 0.878 0.806 0.767 0.768 0.765 0.784 1.496
## [14271] 0.763 0.763 0.762 1.079 0.762 0.761 0.762 0.761 0.761 0.761
## [14281] 0.805 0.764 1.417 1.202 1.564 1.549 1.688 2.058 0.761 0.762
## [14291] 0.763 0.762 0.762 0.762 0.762 0.765 0.762 0.761 0.761 0.762
## [14301] 0.761 0.761 0.762 0.761 0.762 0.761 0.777 0.787 0.795 0.801
## [14311] 0.787 0.800 0.828 0.816 0.803 0.808 0.820 0.790 0.802 0.813
## [14321] 0.840 0.865 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.762
## [14331] 0.762 0.762 0.761 0.855 0.798 0.799 0.815 0.786 1.067 1.162
## [14341] 1.121 1.138 1.135 1.131 1.160 1.186 1.122 1.088 1.140 1.127
## [14351] 1.079 0.762 1.129 0.762 0.761 0.870 0.761 0.760 0.761 0.761
## [14361] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.762 0.761
## [14371] 0.761 0.761 0.764 0.761 0.762 1.511 0.761 0.762 0.762 0.762
## [14381] 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.761
## [14391] 0.761 0.762 1.065 0.761 1.072 1.123 0.761 1.276 1.438 0.760
## [14401] 0.761 0.762 0.763 0.762 0.762 0.762 0.762 0.761 0.762 0.761
## [14411] 0.761 0.762 0.761 0.761 0.761 0.761 0.762 0.761 1.002 0.762
## [14421] 0.761 0.762 0.762 0.762 0.762 4.353 8.238 2.898 5.960 1.663
## [14431] 1.168 0.770 0.983 0.993 0.875 0.780 0.764 0.772 0.774 0.772
## [14441] 0.763 0.764 0.764 0.762 0.762 0.762 0.762 0.763 0.764 0.789
## [14451] 0.765 0.942 0.762 0.801 0.769 0.781 0.819 0.770 0.762 0.762
## [14461] 0.762 3.831 7.223 2.179 2.172 2.733 1.778 3.007 2.739 3.861
## [14471] 4.323 5.778 4.822 2.890 2.680 2.051 2.812 2.180 5.758 2.142
## [14481] 3.706 6.758 4.337 11.107 1.856 1.721 1.361 3.869 1.371 1.216
## [14491] 0.817 0.762 0.761 0.763 5.272 6.878 1.523 1.679 0.877 3.506
## [14501] 2.368 1.662 4.287 2.632 0.958 0.901 1.771 3.610 0.761 4.940
## [14511] 0.952 0.761 0.761 0.761 0.761 1.435 0.761 1.509 0.948 0.762
## [14521] 0.762 0.762 0.762 2.136 10.288 5.504 0.761 1.264 1.010 0.761
## [14531] 0.761 0.761 0.761 3.825 2.962 0.761 8.253 2.152 2.014 3.858
## [14541] 3.813 2.646 6.501 3.706 1.958 1.662 1.875 0.762 1.672 4.986
## [14551] 8.326 6.771 3.151 3.925 1.135 1.360 1.240 1.156 0.761 6.202
## [14561] 6.093 3.201 5.867 2.927 3.934 3.625 4.717 2.414 5.743 2.253
## [14571] 3.027 4.868 1.672 2.686 1.291 5.197 3.668 3.049 2.354 3.948
## [14581] 4.113 4.665 5.629 5.294 2.669 5.755 1.286 3.243 1.808 2.908
## [14591] 6.710 8.595 6.606 3.964 4.516 3.395 3.610 3.104 1.914 2.905
## [14601] 1.512 2.685 0.819 1.182 1.958 2.745 1.886 0.872 3.156 3.407
## [14611] 5.436 5.951 3.141 4.263 4.465 3.914 3.212 2.939 1.540 0.762
## [14621] 0.762 0.761 0.761 0.761 0.761 0.761 0.762 1.091 0.769 0.958
## [14631] 0.811 1.025 1.039 0.761 1.141 0.775 0.761 0.925 0.761 0.761
## [14641] 0.761 0.869 0.761 1.062 0.891 0.796 0.969 0.778 1.017 0.875
## [14651] 1.441 1.594 1.546 1.516 1.514 0.760 0.820 1.916 0.761 0.761
## [14661] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [14671] 0.761 1.164 2.279 0.762 2.051 0.762 0.762 0.762 0.762 0.762
## [14681] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.916 2.236
## [14691] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761
## [14701] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [14711] 0.761 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [14721] 0.762 0.762 0.762 0.762 0.765 0.761 0.761 0.826 0.762 0.762
## [14731] 0.762 0.762 0.762 0.762 0.762 0.815 0.762 0.761 0.761 0.761
## [14741] 1.111 1.330 1.411 0.827 0.761 0.771 0.761 1.054 0.762 0.982
## [14751] 0.762 0.761 0.800 0.761 0.807 0.863 0.811 0.761 0.803 0.761
## [14761] 0.811 0.761 0.810 0.797 0.794 0.807 0.762 0.762 0.762 0.762
## [14771] 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761 0.932 0.761
## [14781] 1.147 0.761 1.116 0.956 1.156 0.761 1.075 1.332 1.134 0.936
## [14791] 1.081 1.130 1.128 1.132 1.020 1.110 1.157 1.123 1.119 1.143
## [14801] 0.866 1.109 1.018 1.404 0.896 1.191 0.772 1.136 0.761 1.106
## [14811] 0.761 1.119 0.761 1.143 0.815 1.003 0.797 0.993 0.773 1.077
## [14821] 0.800 1.006 0.776 1.820 0.959 1.892 1.329 4.095 2.290 1.982
## [14831] 4.089 1.011 0.851 0.761 0.761 1.033 0.761 1.124 0.761 1.095
## [14841] 1.045 1.108 1.031 1.046 1.073 0.764 1.051 1.109 1.115 1.023
## [14851] 0.761 1.157 0.901 1.172 0.862 1.123 0.761 1.087 0.761 0.761
## [14861] 0.761 0.761 0.761 0.761 0.770 1.067 1.145 1.154 1.141 1.069
## [14871] 1.094 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [14881] 0.762 0.761 0.762 2.096 1.496 2.075 1.185 0.762 0.761 0.761
## [14891] 0.761 0.761 0.761 0.761 0.848 0.761 0.761 0.761 0.761 1.030
## [14901] 3.820 3.376 2.338 1.185 0.761 0.761 0.761 0.761 0.761 0.761
## [14911] 0.761 0.761 0.761 0.761 0.761 0.761 1.022 2.371 3.153 2.001
## [14921] 3.050 2.861 1.892 0.997 0.781 1.035 1.517 0.760 0.762 0.761
## [14931] 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.762
## [14941] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.762
## [14951] 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [14961] 0.761 0.762 0.761 0.762 0.760 0.760 0.761 0.760 0.762 0.761
## [14971] 0.760 0.762 0.761 0.760 0.761 0.761 0.760 0.762 0.760 0.761
## [14981] 0.762 0.760 0.761 0.760 0.761 0.761 0.760 0.761 0.762 0.760
## [14991] 0.761 1.133 0.760 0.760 0.761 0.761 0.761 0.761 0.761 0.760
## [15001] 0.761 0.761 0.761 0.761 0.760 0.760 0.760 0.761 0.761 0.761
## [15011] 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.763
## [15021] 0.761 0.761 0.761 0.761 0.760 0.763 0.761 0.761 0.763 0.762
## [15031] 0.761 0.763 0.761 0.762 0.766 0.761 0.766 0.761 0.766 0.761
## [15041] 1.832 0.761 1.336 0.761 0.761 0.761 0.766 0.761 0.761 0.761
## [15051] 0.762 0.761 0.762 0.762 0.762 0.761 0.761 0.760 2.000 0.761
## [15061] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [15071] 0.761 1.313 0.824 0.760 0.844 0.761 0.761 2.546 0.762 3.286
## [15081] 0.762 0.761 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761
## [15091] 0.760 0.761 1.375 0.760 0.762 1.375 0.760 0.761 0.761 0.760
## [15101] 0.761 0.763 0.760 0.760 0.761 0.761 1.083 0.761 0.761 0.761
## [15111] 0.761 0.760 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.762
## [15121] 0.761 0.761 0.762 0.763 0.761 0.761 0.761 0.766 0.762 2.777
## [15131] 1.312 3.636 0.938 0.911 0.913 0.945 0.922 0.976 0.906 0.822
## [15141] 0.787 0.867 0.871 0.980 0.912 0.907 0.864 0.918 0.912 0.887
## [15151] 0.969 0.760 0.760 0.760 0.760 0.760 0.760 0.760 0.760 1.491
## [15161] 0.840 1.006 0.811 0.972 0.926 0.870 0.761 0.903 0.761 0.867
## [15171] 0.762 0.928 0.761 0.925 0.760 0.911 0.762 0.905 0.762 0.940
## [15181] 0.762 0.894 0.761 0.951 0.761 0.951 0.761 0.908 0.761 0.970
## [15191] 0.761 0.906 0.761 0.860 0.761 0.909 0.761 0.933 0.761 0.928
## [15201] 0.766 0.974 0.998 0.845 0.938 0.967 0.952 0.968 1.522 2.140
## [15211] 1.287 1.938 0.762 0.761 0.761 0.762 0.761 0.760 0.762 0.761
## [15221] 0.761 0.761 0.761 0.952 0.925 0.967 0.943 0.927 0.856 0.867
## [15231] 0.953 0.884 0.761 0.923 0.888 0.862 0.827 0.935 0.857 0.916
## [15241] 0.889 0.925 0.925 0.915 0.952 0.880 0.917 0.815 0.882 0.916
## [15251] 0.772 0.835 0.931 0.845 0.807 0.926 0.949 0.801 0.783 0.926
## [15261] 0.938 0.945 0.777 0.879 0.931 0.777 0.761 0.905 0.763 0.762
## [15271] 0.763 0.846 0.761 0.761 0.925 0.809 0.835 0.761 0.925 0.760
## [15281] 0.760 0.784 0.763 0.760 0.762 0.761 0.763 0.760 0.762 0.761
## [15291] 0.763 0.760 0.764 0.774 0.763 0.760 0.761 0.800 0.782 0.760
## [15301] 0.876 0.770 0.797 0.760 1.809 0.763 0.760 0.837 0.761 0.760
## [15311] 0.761 0.767 1.237 0.773 0.761 0.760 0.761 0.841 1.284 0.780
## [15321] 1.307 0.829 1.373 0.782 0.798 0.798 0.761 0.805 0.760 0.761
## [15331] 0.895 0.833 0.761 1.150 0.765 0.761 0.761 0.773 0.935 0.761
## [15341] 0.762 0.761 0.807 0.834 0.797 0.761 0.762 0.832 0.762 0.762
## [15351] 0.762 0.761 0.839 0.761 0.780 0.762 0.760 0.867 0.772 0.764
## [15361] 0.762 0.762 0.896 0.770 0.807 0.762 0.762 0.788 0.762 0.761
## [15371] 0.762 0.762 0.807 0.761 0.828 0.761 0.818 0.763 0.878 0.761
## [15381] 0.804 0.762 0.910 0.761 0.879 0.761 0.933 0.761 0.820 0.761
## [15391] 0.922 0.761 0.768 0.782 0.846 0.761 0.852 0.811 0.958 0.761
## [15401] 0.928 0.761 0.971 0.761 0.825 0.781 0.921 0.761 0.870 0.761
## [15411] 0.893 0.766 0.824 0.780 0.935 0.924 0.913 0.856 0.859 0.940
## [15421] 0.933 0.846 0.884 0.950 0.873 0.791 0.926 0.802 0.761 0.764
## [15431] 0.762 0.762 0.782 0.761 1.376 0.782 0.792 0.794 0.865 0.918
## [15441] 0.844 0.760 0.760 0.937 0.760 0.931 0.940 0.760 0.954 0.866
## [15451] 0.760 0.800 0.866 0.866 1.095 1.525 0.831 0.778 0.760 1.220
## [15461] 0.762 1.009 0.798 0.762 0.846 0.889 0.762 0.830 0.908 0.761
## [15471] 0.761 1.380 0.814 0.761 0.829 0.761 2.053 0.864 0.761 1.342
## [15481] 0.873 0.761 1.474 0.800 0.761 1.411 0.815 0.761 1.314 0.761
## [15491] 0.766 0.940 0.867 0.906 0.927 0.898 0.914 0.930 0.902 0.920
## [15501] 0.929 0.883 0.868 0.918 0.889 0.926 0.894 0.942 0.942 0.760
## [15511] 0.760 0.958 0.762 0.761 0.761 0.761 0.766 0.932 0.903 0.919
## [15521] 2.797 4.270 6.197 3.766 3.937 3.400 2.105 2.117 1.982 1.912
## [15531] 1.826 1.208 1.951 2.383 1.239 2.418 1.520 2.177 1.982 2.304
## [15541] 1.230 1.271 1.335 1.292 1.886 0.762 3.224 2.928 3.258 3.273
## [15551] 2.824 1.145 0.915 1.060 1.218 2.260 2.377 0.761 4.008 0.761
## [15561] 7.483 0.761 10.194 0.761 7.092 1.507 8.171 1.199 7.961 2.512
## [15571] 8.048 0.761 6.367 0.761 5.661 5.727 2.974 3.568 3.919 6.383
## [15581] 2.240 0.761 0.761 0.761 0.761 0.761 0.761 0.762 1.152 1.060
## [15591] 1.068 1.051 1.082 1.097 1.029 1.085 1.006 1.007 1.018 0.926
## [15601] 0.938 1.821 1.018 0.968 1.815 1.786 1.668 2.364 1.704 0.761
## [15611] 0.761 0.761 0.761 0.761 0.761 0.761 1.224 1.168 1.219 0.896
## [15621] 1.254 1.276 1.243 2.690 0.761 1.191 2.251 1.835 1.288 3.220
## [15631] 1.284 1.211 1.327 1.020 2.057 1.563 1.211 0.969 2.515 2.494
## [15641] 1.193 0.888 6.026 1.728 0.911 1.011 5.446 2.549 1.036 0.983
## [15651] 1.534 2.477 0.908 0.888 2.150 0.875 1.335 1.298 1.834 0.925
## [15661] 0.891 1.226 2.661 1.018 0.950 1.680 2.407 1.349 0.957 1.235
## [15671] 1.896 0.867 1.050 1.142 1.633 0.997 0.963 2.424 1.617 0.937
## [15681] 0.869 1.941 1.088 0.881 0.871 1.052 1.605 0.999 1.176 1.236
## [15691] 1.666 1.001 0.858 1.499 1.906 0.851 0.875 1.193 1.594 1.867
## [15701] 0.875 2.387 1.078 0.924 0.874 2.354 1.032 0.843 0.858 1.926
## [15711] 1.834 0.961 0.866 2.310 1.862 0.950 0.870 1.859 1.793 1.590
## [15721] 1.915 3.120 2.501 2.035 1.899 2.789 1.369 2.249 2.491 3.089
## [15731] 0.762 1.475 0.766 3.157 1.990 1.840 1.938 2.064 1.344 1.940
## [15741] 1.523 1.108 3.729 1.977 0.982 2.219 1.608 1.497 0.995 0.761
## [15751] 1.883 0.933 1.292 0.898 1.047 1.640 3.160 1.309 1.058 0.761
## [15761] 2.179 1.907 1.856 3.524 1.191 0.965 1.539 5.260 2.735 0.966
## [15771] 1.481 5.987 2.045 1.519 1.528 5.331 2.187 0.911 1.430 6.991
## [15781] 1.517 0.847 0.936 5.009 2.530 1.099 0.940 4.542 1.530 0.761
## [15791] 0.947 2.980 2.813 0.865 0.951 1.615 2.493 0.827 0.917 1.528
## [15801] 1.641 1.172 0.966 1.505 2.162 0.820 0.879 1.534 1.283 0.764
## [15811] 1.190 1.530 1.904 1.062 0.953 0.873 1.343 1.406 0.761 1.072
## [15821] 0.761 0.941 2.087 1.056 0.761 0.982 0.761 1.026 1.182 1.019
## [15831] 0.761 1.002 0.761 1.145 0.761 1.034 0.761 0.912 0.761 1.036
## [15841] 1.238 1.575 0.761 0.939 1.849 0.906 2.477 1.530 1.195 1.054
## [15851] 0.762 1.039 1.228 1.072 1.232 1.337 1.316 1.380 2.840 2.536
## [15861] 2.294 3.693 3.082 3.394 2.465 2.180 3.043 3.703 3.192 3.673
## [15871] 3.130 3.101 3.422 2.900 3.472 3.299 2.863 3.122 2.281 2.662
## [15881] 2.926 2.280 1.575 0.761 1.909 1.971 3.102 2.304 1.248 6.410
## [15891] 1.634 1.001 2.913 2.238 1.072 3.631 1.277 0.973 0.761 1.432
## [15901] 0.979 3.439 0.990 0.950 3.186 1.295 0.872 3.703 1.278 0.761
## [15911] 4.658 1.102 0.991 5.929 1.139 0.973 3.439 1.286 2.347 1.011
## [15921] 0.994 0.846 1.498 1.030 1.241 1.128 2.731 0.764 1.431 4.574
## [15931] 1.742 1.970 1.520 2.832 1.721 5.992 1.477 1.496 0.761 1.496
## [15941] 1.522 1.496 0.762 0.761 1.618 0.761 0.761 1.218 0.761 1.623
## [15951] 1.060 2.267 1.456 1.564 1.242 1.251 0.996 1.655 1.977 3.533
## [15961] 2.102 2.501 2.856 2.883 2.716 2.779 2.438 2.111 2.869 2.911
## [15971] 1.587 1.412 2.269 1.398 1.484 3.349 3.366 2.444 0.761 1.902
## [15981] 1.481 1.525 1.341 2.239 2.001 0.761 1.516 1.496 0.761 0.761
## [15991] 0.761 0.845 0.987 0.761 0.896 1.007 0.761 0.761 1.676 0.761
## [16001] 0.892 1.010 0.761 0.908 1.029 0.762 0.761 1.329 1.181 0.761
## [16011] 1.070 1.029 0.761 0.992 0.791 0.761 1.073 0.999 0.761 0.978
## [16021] 1.042 0.761 2.994 1.082 0.761 1.862 1.012 0.763 2.735 0.835
## [16031] 0.761 2.434 1.173 0.761 2.538 0.820 0.761 0.975 0.851 0.761
## [16041] 3.063 0.761 0.761 0.822 0.805 0.761 2.024 0.761 0.798 0.761
## [16051] 2.292 0.831 0.761 0.761 2.982 0.815 0.762 0.922 3.153 0.761
## [16061] 0.961 0.761 1.118 3.020 0.848 1.449 0.948 1.380 0.761 1.163
## [16071] 6.871 1.687 1.574 0.851 1.401 1.626 1.278 3.692 1.785 3.801
## [16081] 2.259 0.877 1.919 1.044 0.904 2.386 2.142 1.247 1.181 3.859
## [16091] 0.962 0.997 0.805 0.761 0.944 0.802 0.761 1.036 0.810 0.761
## [16101] 1.161 0.826 0.761 1.184 0.908 0.761 1.085 1.182 0.761 1.036
## [16111] 1.675 0.761 1.058 0.882 0.940 0.765 0.932 0.761 0.790 0.822
## [16121] 0.761 0.870 0.761 0.911 0.833 0.815 0.888 0.849 0.809 0.995
## [16131] 0.778 0.868 0.761 0.761 1.142 0.761 0.988 1.110 0.761 0.761
## [16141] 0.983 0.761 0.761 0.817 0.762 0.762 0.876 0.793 0.775 0.801
## [16151] 0.891 0.887 0.777 0.801 0.848 0.803 0.805 0.765 0.768 0.769
## [16161] 0.770 0.769 0.773 0.768 0.777 0.804 0.842 0.839 0.776 0.764
## [16171] 0.763 0.767 0.768 0.769 0.769 0.768 0.772 0.766 0.766 0.765
## [16181] 0.764 0.764 0.764 0.765 0.776 0.767 0.774 0.763 0.765 0.765
## [16191] 0.763 0.777 0.765 0.765 0.766 0.764 0.765 0.765 0.786 0.762
## [16201] 0.764 0.763 0.763 0.767 0.763 0.763 0.763 0.763 0.764 0.763
## [16211] 0.763 0.768 0.764 0.763 0.762 0.762 0.771 0.783 0.809 0.854
## [16221] 0.881 0.895 0.899 0.930 0.807 0.805 0.773 0.775 0.812 0.805
## [16231] 0.795 0.790 0.833 0.819 0.807 0.761 0.761 0.761 0.761 0.761
## [16241] 0.793 0.790 0.766 0.766 0.776 0.824 0.779 0.771 0.768 0.762
## [16251] 0.762 0.764 0.777 0.773 0.772 0.766 0.769 0.765 0.770 0.765
## [16261] 0.774 0.774 0.764 0.763 0.790 0.794 0.761 0.774 0.990 0.847
## [16271] 1.051 1.028 0.955 1.037 1.011 1.001 1.111 1.429 1.280 1.402
## [16281] 1.174 1.101 1.371 1.426 1.279 1.655 1.179 1.439 3.014 0.771
## [16291] 0.763 0.765 0.767 0.765 0.768 0.794 0.833 0.826 0.881 0.869
## [16301] 0.846 0.831 0.869 0.784 0.783 0.789 0.786 0.779 0.800 0.794
## [16311] 0.823 0.855 0.761 0.761 0.761 0.761 0.763 0.845 0.904 0.927
## [16321] 0.763 0.762 0.762 0.822 0.823 0.783 0.868 0.858 0.761 0.844
## [16331] 0.790 0.955 0.777 0.894 0.807 0.890 0.862 1.079 0.761 0.761
## [16341] 0.761 0.800 0.762 0.761 0.762 0.764 0.765 0.762 0.762 0.818
## [16351] 0.882 0.914 0.892 0.888 0.900 0.793 0.786 0.768 0.767 0.771
## [16361] 0.762 0.770 0.766 0.770 0.765 0.766 0.770 0.765 0.765 0.900
## [16371] 0.881 0.761 0.761 0.851 0.818 0.849 0.782 0.813 0.854 0.854
## [16381] 0.853 0.882 0.808 0.778 0.763 0.761 0.762 0.820 0.826 0.870
## [16391] 0.871 0.926 0.919 0.928 0.891 0.772 0.816 0.761 0.761 0.796
## [16401] 0.790 0.825 0.834 0.831 0.871 0.821 0.827 0.852 0.986 0.938
## [16411] 0.795 0.888 0.833 0.831 0.867 1.120 0.820 0.881 0.866 0.845
## [16421] 0.841 0.832 0.845 0.858 0.859 0.819 0.842 0.838 0.826 0.852
## [16431] 0.846 0.841 0.855 0.883 0.848 0.906 0.880 0.871 0.876 0.895
## [16441] 0.847 0.919 1.017 0.829 0.814 0.897 0.842 0.774 0.814 0.803
## [16451] 0.839 0.922 0.913 0.951 0.977 0.943 0.948 0.859 1.063 1.027
## [16461] 1.114 0.833 0.905 0.948 1.006 0.826 1.003 1.032 0.975 1.060
## [16471] 0.944 1.019 0.921 0.937 0.902 0.906 0.912 0.953 0.917 0.936
## [16481] 0.957 0.936 0.925 0.944 0.762 0.857 0.916 0.761 0.891 0.911
## [16491] 0.843 0.923 1.000 0.902 0.999 1.065 1.094 0.969 1.064 0.910
## [16501] 0.928 1.018 0.912 1.017 1.060 0.904 1.042 0.999 1.002 1.060
## [16511] 1.124 0.942 1.016 1.199 0.927 1.086 1.054 1.073 1.121 1.085
## [16521] 0.968 0.945 1.076 0.977 1.050 1.036 0.988 1.078 1.086 1.048
## [16531] 1.175 1.064 1.055 1.092 1.326 1.057 1.060 1.171 1.306 1.065
## [16541] 1.251 1.079 1.005 1.327 1.067 1.021 0.971 1.089 1.123 0.925
## [16551] 0.931 0.775 0.964 1.172 1.002 1.005 1.172 1.167 1.518 1.441
## [16561] 1.106 1.079 1.041 1.085 1.105 1.012 1.008 1.024 1.009 1.007
## [16571] 1.074 1.010 1.083 1.040 1.003 1.093 0.761 0.761 0.761 0.761
## [16581] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [16591] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [16601] 0.762 0.863 0.761 0.868 0.761 0.760 0.761 0.761 0.760 0.761
## [16611] 0.831 0.840 0.761 0.825 0.761 0.854 0.761 0.860 0.761 0.847
## [16621] 0.761 0.831 0.761 0.828 0.761 0.838 0.761 0.838 0.761 1.510
## [16631] 0.761 1.299 0.761 1.536 0.761 1.808 0.761 1.460 1.552 1.554
## [16641] 0.761 1.537 1.556 1.563 1.566 1.592 1.610 0.761 1.536 1.600
## [16651] 1.575 1.506 1.537 1.556 1.585 1.554 1.574 1.600 1.556 1.568
## [16661] 1.206 0.994 0.761 1.010 0.761 0.899 0.872 0.880 0.811 0.883
## [16671] 0.889 0.761 0.868 1.035 0.831 0.901 0.854 0.796 0.834 0.761
## [16681] 0.855 0.761 0.885 0.761 0.839 1.007 0.838 0.946 0.844 0.761
## [16691] 0.818 0.778 0.801 0.885 0.972 0.944 0.951 0.972 0.975 0.951
## [16701] 0.899 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [16711] 0.761 0.761 0.761 0.763 0.761 0.761 0.761 0.761 0.761 0.761
## [16721] 0.761 0.761 0.761 0.761 0.770 0.766 0.761 0.761 0.761 0.761
## [16731] 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762
## [16741] 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.762
## [16751] 0.761 0.762 0.761 0.761 0.761 0.761 0.763 0.762 0.761 0.761
## [16761] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [16771] 0.761 0.762 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762
## [16781] 0.762 0.762 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.761
## [16791] 0.761 0.761 0.761 0.824 0.761 0.781 0.762 0.762 0.761 0.762
## [16801] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.762
## [16811] 0.761 0.920 0.843 0.867 0.783 0.915 0.799 0.782 0.811 0.860
## [16821] 0.842 0.915 0.837 0.809 0.843 0.855 0.791 0.967 0.906 0.867
## [16831] 1.809 0.907 1.584 0.806 1.673 0.788 1.747 0.867 1.761 0.868
## [16841] 1.754 0.921 1.838 0.941 1.845 0.936 1.794 0.953 1.721 0.975
## [16851] 1.713 1.041 1.967 0.898 1.914 0.933 2.005 0.871 1.635 0.991
## [16861] 1.265 1.902 1.628 1.606 1.462 1.206 1.714 1.901 1.623 1.638
## [16871] 1.514 0.981 0.907 0.860 0.986 0.842 0.888 0.786 0.845 0.785
## [16881] 0.910 0.784 0.951 0.831 0.842 0.861 0.921 0.880 1.084 0.911
## [16891] 0.903 0.896 0.909 0.771 0.954 0.816 0.985 0.830 0.903 0.800
## [16901] 0.899 0.761 0.761 0.861 0.865 0.761 0.761 1.922 1.811 1.437
## [16911] 1.942 1.713 2.046 2.167 1.646 2.132 1.539 2.442 2.140 2.281
## [16921] 2.184 1.103 2.361 2.280 2.179 2.039 1.840 2.814 2.307 2.654
## [16931] 1.861 3.452 2.322 3.316 2.201 3.653 2.260 3.723 2.778 4.404
## [16941] 2.534 5.732 0.907 2.261 2.365 2.593 2.678 2.650 3.306 1.907
## [16951] 4.226 1.762 3.440 2.742 3.663 6.081 2.802 4.764 1.744 1.959
## [16961] 3.817 3.287 5.968 5.266 2.224 2.787 4.480 7.055 5.270 5.713
## [16971] 5.303 3.681 4.717 4.598 2.848 2.966 3.109 3.156 2.149 2.399
## [16981] 2.170 2.131 1.948 1.754 1.414 2.439 1.116 1.668 1.551 1.701
## [16991] 0.800 1.809 2.239 1.639 2.078 2.055 1.990 2.532 2.691 1.126
## [17001] 2.643 2.512 2.457 3.262 2.176 2.457 2.831 1.820 1.738 1.540
## [17011] 1.628 1.777 1.792 1.533 0.762 0.762 0.762 0.762 0.762 0.762
## [17021] 0.762 0.762 0.762 0.813 1.084 1.168 1.588 1.493 1.335 1.374
## [17031] 1.344 1.587 1.212 1.523 0.761 0.762 1.528 0.761 1.294 1.989
## [17041] 2.813 1.344 1.628 1.572 1.484 2.521 5.745 5.690 2.718 5.975
## [17051] 8.509 7.192 5.954 1.774 2.408 1.490 1.702 1.012 1.149 1.263
## [17061] 0.942 1.660 1.884 2.303 1.694 1.190 2.081 2.249 2.181 2.177
## [17071] 2.069 2.241 1.970 1.962 2.166 1.979 2.315 2.551 1.015 2.226
## [17081] 2.924 3.231 3.726 2.565 4.009 3.730 1.693 2.471 1.030 2.132
## [17091] 2.102 2.178 1.635 0.924 1.605 1.684 1.823 1.601 2.066 1.809
## [17101] 1.593 2.401 1.373 2.106 2.309 2.154 2.369 1.878 2.413 2.318
## [17111] 2.137 1.941 2.042 3.060 2.166 3.072 2.008 3.622 2.164 3.535
## [17121] 2.312 3.368 2.348 3.537 2.827 4.070 2.231 2.893 1.108 1.854
## [17131] 2.252 3.087 2.584 3.095 2.072 4.020 4.407 4.442 4.619 3.443
## [17141] 7.920 9.410 1.980 2.305 3.841 2.924 3.970 3.106 6.181 5.258
## [17151] 1.706 3.770 8.613 3.881 2.934 2.011 3.122 1.884 2.828 4.406
## [17161] 1.542 2.369 2.604 2.510 1.738 1.720 2.178 1.798 1.648 1.723
## [17171] 2.037 1.300 1.631 1.519 1.666 2.058 2.074 2.160 1.704 2.075
## [17181] 1.978 2.756 2.973 3.319 3.690 3.336 3.755 2.869 2.280 1.587
## [17191] 3.990 5.722 3.290 5.035 3.311 5.308 2.129 2.087 0.891 1.002
## [17201] 0.902 1.227 1.082 1.313 1.097 1.763 2.243 1.564 0.973 1.658
## [17211] 0.826 1.983 0.842 1.984 0.826 2.127 1.273 1.710 1.633 1.804
## [17221] 2.633 2.665 0.762 0.761 1.393 0.761 1.243 1.392 2.179 2.992
## [17231] 3.018 2.211 1.843 5.192 1.953 3.112 1.884 2.699 1.334 1.273
## [17241] 9.000 8.350 2.638 3.236 2.681 4.854 3.304 1.740 3.152 2.870
## [17251] 1.371 1.574 1.450 0.999 1.465 1.634 1.351 1.290 1.263 1.020
## [17261] 1.028 2.058 7.328 0.762 0.762 0.762 0.762 0.761 0.762 0.762
## [17271] 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761
## [17281] 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762
## [17291] 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [17301] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [17311] 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [17321] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [17331] 0.761 0.762 0.762 0.762 1.358 0.762 0.762 0.762 0.761 0.762
## [17341] 0.762 0.762 1.540 0.762 0.762 0.762 1.228 0.762 0.762 0.762
## [17351] 1.345 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [17361] 0.762 0.762 1.361 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [17371] 1.501 0.762 0.762 1.110 2.193 0.762 0.762 1.397 1.163 0.761
## [17381] 0.762 2.184 1.383 0.762 0.762 2.098 2.215 0.761 1.035 0.762
## [17391] 1.308 0.762 1.438 2.111 1.716 1.639 0.761 1.471 1.485 0.761
## [17401] 1.376 1.920 0.762 0.761 0.762 0.762 1.481 0.762 1.520 0.762
## [17411] 0.762 2.273 0.762 0.762 0.762 0.762 1.292 0.762 1.439 0.762
## [17421] 1.473 1.153 0.762 0.762 1.202 0.858 0.762 0.762 1.095 0.762
## [17431] 0.762 0.762 1.462 0.762 0.762 0.762 1.390 0.762 0.762 0.762
## [17441] 1.525 0.762 0.762 0.762 1.941 0.762 0.762 0.762 0.916 0.762
## [17451] 0.762 0.762 1.017 0.762 0.762 0.762 1.453 0.762 1.471 0.762
## [17461] 0.761 0.762 1.445 1.458 1.230 0.762 1.278 0.762 0.761 0.762
## [17471] 0.762 0.762 0.761 0.762 0.762 0.761 1.517 0.761 0.761 0.762
## [17481] 0.761 0.761 0.762 1.516 0.761 1.211 0.762 1.367 0.762 0.761
## [17491] 1.100 1.512 0.761 1.392 0.761 0.761 0.957 0.762 0.761 1.528
## [17501] 1.198 1.171 0.761 0.762 1.285 0.819 0.761 0.762 0.999 1.051
## [17511] 1.527 0.762 1.527 0.762 0.761 1.311 0.761 0.762 0.761 0.761
## [17521] 1.078 1.062 0.761 0.761 1.363 1.524 0.761 0.761 1.229 1.522
## [17531] 0.761 1.524 0.761 0.824 1.329 1.510 0.761 0.761 0.762 0.762
## [17541] 1.431 0.761 0.762 0.762 1.636 1.491 0.762 1.507 0.762 1.017
## [17551] 1.094 0.761 0.762 1.115 0.762 1.267 1.516 1.944 1.505 0.762
## [17561] 0.762 1.517 0.761 0.762 0.762 1.524 0.761 1.268 0.762 0.762
## [17571] 0.761 0.762 0.762 0.762 0.761 1.513 0.762 1.523 1.446 0.762
## [17581] 0.762 2.131 0.761 0.762 0.762 1.500 0.762 0.762 1.169 0.761
## [17591] 1.090 0.762 1.256 0.904 0.762 1.523 1.517 0.761 0.761 0.761
## [17601] 0.761 0.761 0.761 1.202 0.761 0.761 1.061 1.321 1.393 0.761
## [17611] 0.871 0.761 1.272 1.466 0.761 1.376 2.139 1.287 1.806 0.761
## [17621] 0.761 1.432 2.278 0.761 1.341 2.942 0.762 0.761 1.497 2.283
## [17631] 0.761 1.527 1.028 1.920 1.416 1.985 0.762 0.761 0.761 1.209
## [17641] 1.517 0.761 0.761 1.131 0.762 0.761 0.761 0.761 0.761 0.761
## [17651] 0.762 1.517 1.509 0.761 0.761 0.762 0.761 1.518 1.082 0.761
## [17661] 0.762 1.751 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.762
## [17671] 1.324 0.762 1.835 0.762 0.762 0.762 0.762 0.762 1.483 0.762
## [17681] 0.761 0.762 0.761 0.762 1.469 0.762 1.517 0.762 1.163 0.949
## [17691] 0.761 0.888 1.483 0.762 0.762 0.762 1.476 0.762 1.606 0.762
## [17701] 1.663 1.528 0.761 1.114 1.754 0.762 1.527 0.936 2.255 0.761
## [17711] 1.987 0.761 3.135 1.144 1.931 1.272 0.761 0.762 1.339 0.762
## [17721] 0.886 1.487 2.576 1.425 1.902 1.395 2.239 0.762 0.761 2.182
## [17731] 1.434 1.950 2.313 0.762 1.411 2.120 0.761 1.802 1.528 0.762
## [17741] 0.761 0.761 1.238 0.761 0.761 0.761 0.761 0.761 0.761 0.762
## [17751] 1.351 0.761 0.762 0.762 0.762 0.761 0.762 1.414 0.761 0.762
## [17761] 1.518 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.762 0.761
## [17771] 0.761 1.172 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.762
## [17781] 1.242 0.761 1.285 1.774 0.762 0.761 0.761 1.154 0.762 0.761
## [17791] 0.761 0.762 0.762 1.526 0.761 0.762 0.762 1.466 0.761 1.095
## [17801] 0.762 0.761 1.345 0.762 0.761 1.756 0.761 0.762 0.762 1.193
## [17811] 1.864 0.762 1.501 1.071 0.761 0.958 1.438 1.370 1.734 0.762
## [17821] 0.762 1.526 2.419 0.762 0.762 1.509 2.256 0.762 0.761 0.762
## [17831] 1.313 1.311 0.762 0.762 1.828 0.761 0.762 1.527 0.978 1.396
## [17841] 0.762 1.489 2.019 1.552 0.762 1.895 1.807 0.762 2.140 1.518
## [17851] 1.500 0.976 0.761 0.762 0.762 0.761 1.506 0.761 1.846 0.761
## [17861] 0.761 1.499 1.781 0.761 2.106 0.761 0.761 0.906 1.953 1.524
## [17871] 0.761 1.492 1.086 0.761 0.761 1.834 0.761 2.236 2.171 0.761
## [17881] 0.761 1.325 2.256 1.327 1.913 0.761 0.761 1.506 0.761 0.761
## [17891] 1.447 1.850 0.761 0.761 1.458 3.027 0.762 1.485 2.218 1.419
## [17901] 1.249 2.290 2.032 0.761 1.527 2.181 0.761 1.479 1.522 0.762
## [17911] 0.761 0.761 1.878 1.896 0.761 1.051 1.198 1.484 0.761 0.761
## [17921] 0.761 0.761 0.762 0.761 0.762 0.762 0.761 0.762 0.761 0.761
## [17931] 0.762 0.761 0.762 0.762 0.761 0.762 1.510 0.761 0.762 0.761
## [17941] 0.761 0.762 1.410 1.468 0.761 0.762 1.041 0.762 0.761 0.762
## [17951] 0.761 0.762 0.761 0.762 1.198 0.762 0.761 0.762 0.761 0.762
## [17961] 0.761 0.762 1.374 0.762 0.761 0.762 0.761 0.762 0.761 1.426
## [17971] 0.761 1.077 0.761 0.761 1.424 0.761 0.761 1.522 0.902 0.762
## [17981] 0.762 0.761 2.384 1.389 0.762 0.761 0.762 0.762 1.190 0.761
## [17991] 0.762 0.762 0.762 1.810 0.762 1.180 0.762 1.088 1.429 0.761
## [18001] 0.762 0.761 1.526 1.405 0.762 1.524 1.232 0.762 1.602 1.523
## [18011] 1.374 0.762 2.134 0.762 0.761 0.762 0.762 0.762 0.761 1.402
## [18021] 0.762 0.762 1.457 0.761 1.160 2.279 0.762 1.519 1.941 0.905
## [18031] 0.895 1.424 1.518 1.286 0.762 1.505 1.532 2.400 2.278 1.476
## [18041] 0.762 0.761 0.762 1.527 1.325 1.988 1.392 0.761 2.274 0.761
## [18051] 1.343 0.761 2.705 0.761 1.944 1.516 1.047 2.351 1.463 1.176
## [18061] 1.321 0.761 0.845 1.522 1.449 0.761 1.523 1.467 1.425 1.475
## [18071] 1.123 2.342 1.510 1.373 1.958 0.761 1.438 1.311 1.017 1.528
## [18081] 0.945 1.511 1.886 1.411 1.367 0.762 1.028 0.761 0.762 1.437
## [18091] 2.794 1.341 0.762 0.762 0.761 1.260 0.762 0.762 2.263 0.761
## [18101] 0.761 1.496 1.492 0.761 1.374 1.726 0.762 0.761 0.761 0.762
## [18111] 0.761 1.350 0.761 0.761 1.521 0.761 1.517 0.761 0.762 0.761
## [18121] 0.762 0.761 1.284 0.761 0.761 0.933 0.761 0.761 1.180 0.761
## [18131] 0.761 0.762 0.761 0.761 1.065 0.761 0.761 1.496 1.411 0.761
## [18141] 0.761 0.826 0.761 0.761 1.097 0.761 0.761 0.761 0.762 0.761
## [18151] 0.761 0.761 0.762 0.761 0.761 1.278 0.762 0.761 0.761 1.260
## [18161] 0.762 0.761 0.761 1.507 0.762 0.761 0.761 1.517 0.762 0.761
## [18171] 0.761 1.104 0.762 0.761 0.761 0.762 0.761 0.761 0.910 0.762
## [18181] 0.761 0.761 0.761 1.446 1.465 0.761 1.407 0.762 0.761 1.763
## [18191] 0.761 0.762 0.761 1.680 2.169 0.762 0.761 0.762 2.056 1.411
## [18201] 0.761 0.762 1.637 0.761 0.761 1.527 0.761 0.761 0.761 1.322
## [18211] 2.267 0.761 0.761 1.401 0.762 0.761 0.761 0.761 0.762 0.761
## [18221] 0.761 0.761 1.171 0.761 0.761 1.387 0.762 0.761 0.761 1.667
## [18231] 0.762 0.761 0.761 1.507 1.315 0.761 0.761 1.104 0.761 0.761
## [18241] 0.761 1.228 1.111 0.761 0.761 1.198 1.795 0.761 0.761 1.278
## [18251] 0.761 0.761 0.761 2.176 0.761 0.761 0.761 0.761 0.761 0.761
## [18261] 0.761 0.761 0.761 1.938 0.761 0.761 0.761 1.039 0.761 0.761
## [18271] 0.761 0.761 1.526 0.761 0.761 1.929 1.475 0.761 0.761 1.268
## [18281] 0.761 0.761 0.761 1.404 0.761 0.761 0.761 1.299 0.761 0.761
## [18291] 0.761 1.368 2.170 0.761 1.380 1.080 2.852 0.761 1.396 2.239
## [18301] 0.761 0.761 1.744 2.031 0.761 0.761 2.272 0.761 0.761 1.372
## [18311] 1.375 0.761 0.761 1.269 1.138 0.761 0.761 0.761 2.199 0.761
## [18321] 0.761 1.522 0.761 0.761 1.497 0.761 1.216 0.761 1.521 1.509
## [18331] 0.761 1.506 0.762 1.419 0.761 0.762 0.887 1.032 0.762 1.894
## [18341] 0.762 0.762 1.526 1.176 0.762 1.475 0.761 0.762 0.761 0.761
## [18351] 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.762 0.963 1.507
## [18361] 0.762 0.762 0.762 0.762 0.762 1.810 0.762 1.248 0.762 1.397
## [18371] 0.762 0.761 0.762 0.762 1.518 0.762 0.762 1.368 0.762 0.762
## [18381] 1.045 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 1.497
## [18391] 0.762 0.762 1.183 0.762 1.518 0.762 2.116 0.762 0.761 1.527
## [18401] 0.829 0.762 0.761 1.520 1.176 0.762 0.761 0.762 0.762 1.425
## [18411] 0.761 0.762 1.268 0.761 1.386 0.762 1.379 1.315 0.762 0.762
## [18421] 0.761 0.762 1.497 1.129 0.761 0.762 1.447 1.465 1.231 0.761
## [18431] 1.261 0.761 2.411 0.762 0.762 2.157 0.761 1.170 0.762 0.762
## [18441] 0.761 0.762 0.851 0.762 1.528 1.231 1.508 1.668 0.761 1.974
## [18451] 1.527 1.458 0.761 1.343 0.762 0.761 1.028 0.761 0.762 0.762
## [18461] 1.342 1.179 0.761 0.762 1.510 0.762 0.761 0.761 0.762 0.762
## [18471] 1.260 0.762 0.762 1.193 0.900 0.762 0.762 1.412 0.762 0.762
## [18481] 0.762 1.427 0.762 1.517 0.762 0.762 0.762 2.290 0.762 0.762
## [18491] 1.447 0.762 0.762 1.366 0.761 1.217 1.517 1.764 1.252 1.217
## [18501] 1.411 1.525 0.761 0.761 1.523 0.762 0.761 0.761 1.527 1.517
## [18511] 0.761 0.762 0.761 0.762 0.762 0.761 0.761 1.197 1.033 1.032
## [18521] 1.311 0.857 0.882 0.774 0.761 1.383 0.761 1.411 1.325 0.946
## [18531] 0.761 0.850 0.761 0.761 0.880 0.761 0.761 0.946 0.869 0.761
## [18541] 0.761 1.392 1.107 1.042 0.761 1.996 0.899 0.925 0.761 2.256
## [18551] 0.838 0.761 0.761 2.155 0.883 0.761 0.761 1.230 0.761 0.761
## [18561] 0.761 1.404 0.761 0.761 0.762 0.761 1.526 0.761 0.762 0.761
## [18571] 0.762 0.761 1.032 1.593 0.762 0.762 0.762 1.225 0.761 1.248
## [18581] 0.762 0.761 0.857 1.522 1.205 1.009 0.761 1.013 1.527 0.761
## [18591] 0.761 1.394 0.761 1.042 1.802 2.115 0.761 0.761 0.761 0.762
## [18601] 0.761 0.761 0.761 0.762 1.395 0.761 0.762 0.761 0.761 0.761
## [18611] 0.762 1.459 0.761 0.978 1.042 1.527 0.957 0.761 0.762 0.761
## [18621] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18631] 0.761 0.761 1.524 0.761 0.761 0.761 0.761 1.211 1.343 0.761
## [18641] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.822 0.761
## [18651] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18661] 1.163 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18671] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18681] 0.761 0.761 0.761 0.761 0.761 0.761 1.646 0.761 1.103 0.761
## [18691] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [18701] 1.197 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [18711] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.761
## [18721] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [18731] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [18741] 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761
## [18751] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18761] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761
## [18771] 0.761 0.987 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.922
## [18781] 0.761 0.761 0.761 2.120 0.761 0.761 0.761 0.762 0.761 0.761
## [18791] 0.762 0.761 0.761 0.762 0.761 1.399 0.762 0.761 0.761 0.762
## [18801] 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18811] 0.761 0.761 0.761 0.761 0.761 0.761 1.524 0.761 0.761 0.761
## [18821] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18831] 0.761 0.761 1.812 0.761 0.761 1.446 0.761 0.761 1.445 0.761
## [18841] 0.761 2.194 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18851] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [18861] 0.761 1.489 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [18871] 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [18881] 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761
## [18891] 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [18901] 1.434 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761
## [18911] 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [18921] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 1.371 1.528 0.761
## [18931] 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [18941] 1.210 0.761 0.761 1.382 0.761 0.761 0.761 0.761 0.761 0.761
## [18951] 0.761 0.761 0.761 0.761 0.761 1.483 0.761 0.761 0.761 0.762
## [18961] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [18971] 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [18981] 0.761 0.761 0.761 0.762 0.761 1.164 0.761 0.762 0.761 1.026
## [18991] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 1.046 0.761 0.761
## [19001] 0.761 1.528 0.761 0.761 0.761 0.761 0.761 1.518 0.761 0.761
## [19011] 0.761 0.761 0.761 0.761 1.190 0.761 0.761 0.761 0.761 1.341
## [19021] 0.762 0.761 0.761 0.761 0.761 1.528 0.761 1.448 0.853 0.761
## [19031] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [19041] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [19051] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.904 0.761 0.761
## [19061] 0.761 1.341 0.761 0.761 0.761 1.503 0.761 0.761 0.762 0.762
## [19071] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.762 0.761 0.761
## [19081] 0.762 0.762 0.761 0.761 0.761 0.762 0.761 0.762 0.761 0.761
## [19091] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761
## [19101] 0.762 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761
## [19111] 0.762 0.761 0.761 0.761 0.976 0.761 0.761 0.761 0.920 0.761
## [19121] 0.761 0.761 0.761 0.761 1.053 0.761 0.761 2.232 1.521 0.761
## [19131] 1.767 1.519 0.761 1.862 1.392 0.761 0.762 2.180 1.508 0.761
## [19141] 1.301 1.520 0.761 2.582 0.761 0.761 1.522 0.761 0.761 1.519
## [19151] 1.505 0.761 0.761 1.769 0.761 1.523 1.470 1.516 0.761 1.474
## [19161] 0.761 1.448 0.761 1.181 2.145 0.761 0.761 1.970 0.761 0.762
## [19171] 1.467 2.186 1.491 1.517 1.386 1.528 0.761 1.507 1.522 0.761
## [19181] 0.761 1.517 1.517 0.761 2.167 1.847 0.761 0.761 0.761 0.761
## [19191] 0.761 0.761 0.761 0.761 1.520 0.761 0.761 0.761 0.761 0.761
## [19201] 0.761 1.386 0.761 0.761 1.260 0.761 1.389 0.890 0.761 1.527
## [19211] 1.157 0.761 0.864 0.762 1.117 0.761 0.761 0.899 0.761 0.761
## [19221] 0.762 1.709 0.761 0.761 0.762 0.762 0.761 0.761 1.269 0.761
## [19231] 0.761 0.761 1.426 0.761 0.761 0.762 0.762 0.848 0.761 0.762
## [19241] 1.655 0.761 0.761 1.020 1.357 0.761 0.761 0.761 1.335 0.761
## [19251] 1.506 0.761 1.758 0.761 1.467 0.761 1.040 1.486 1.293 0.761
## [19261] 0.904 0.762 0.762 1.411 0.761 0.762 1.241 0.762 1.683 0.762
## [19271] 0.761 0.761 1.106 0.897 1.492 1.523 1.370 0.761 1.431 0.761
## [19281] 1.170 0.761 0.762 0.761 1.520 0.761 1.424 0.761 1.484 1.524
## [19291] 0.761 0.761 1.517 0.762 1.062 1.528 0.762 0.762 1.525 1.509
## [19301] 2.275 1.526 1.500 1.410 0.761 0.761 0.762 0.761 1.232 0.762
## [19311] 1.380 0.761 0.761 1.183 1.505 0.761 0.761 1.140 0.761 1.504
## [19321] 1.972 1.519 1.497 0.761 0.761 1.358 0.761 1.444 1.343 0.761
## [19331] 1.474 1.510 1.473 0.761 0.762 1.037 1.266 0.761 1.470 0.761
## [19341] 1.417 0.761 1.050 0.761 0.762 0.761 0.848 0.761 0.762 0.761
## [19351] 1.130 0.761 1.373 0.761 1.230 1.505 1.373 1.509 1.417 1.528
## [19361] 0.762 1.523 1.516 1.268 1.420 1.430 1.984 0.762 0.762 1.383
## [19371] 1.524 1.341 1.626 0.761 1.527 1.410 1.763 1.462 1.467 0.762
## [19381] 0.762 1.424 1.446 0.761 1.468 1.482 0.762 0.761 0.761 1.290
## [19391] 0.762 1.474 0.761 0.762 1.412 0.761 0.761 0.762 0.761 0.762
## [19401] 0.761 0.762 0.761 1.462 0.761 0.761 0.761 1.451 0.761 0.762
## [19411] 0.761 1.410 1.290 0.761 0.762 0.761 0.762 0.761 0.762 0.761
## [19421] 0.762 0.761 0.762 0.761 0.762 0.761 0.762 1.032 0.762 0.762
## [19431] 0.761 2.075 0.762 0.762 0.762 0.761 0.761 0.762 0.762 0.762
## [19441] 0.762 0.762 0.762 0.761 0.761 1.525 1.503 0.761 0.761 1.322
## [19451] 1.362 1.306 1.553 1.423 0.761 1.516 0.761 0.819 0.761 0.762
## [19461] 0.850 0.762 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762
## [19471] 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.762
## [19481] 0.761 0.762 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761
## [19491] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.761
## [19501] 0.761 0.762 1.197 0.761 0.761 0.762 0.762 0.761 0.761 0.761
## [19511] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [19521] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [19531] 0.762 0.761 1.523 0.761 1.485 0.761 1.522 0.761 0.761 0.761
## [19541] 0.761 0.761 0.761 1.049 0.761 0.761 0.827 1.374 1.479 0.762
## [19551] 1.634 0.761 1.351 1.512 0.761 1.392 1.311 1.232 1.941 1.506
## [19561] 1.351 1.490 1.900 0.761 0.762 1.527 1.508 1.510 0.762 0.761
## [19571] 1.474 1.341 0.888 0.761 0.762 1.374 0.762 0.761 0.762 0.761
## [19581] 1.523 0.761 0.762 1.475 1.446 0.761 0.762 0.761 0.826 0.761
## [19591] 1.170 0.762 0.762 0.761 2.459 1.526 0.761 1.965 0.761 0.761
## [19601] 0.761 1.452 0.761 1.516 0.761 0.761 1.411 0.761 0.761 0.761
## [19611] 0.761 0.761 1.164 0.761 0.761 1.497 0.761 1.524 0.976 0.761
## [19621] 0.761 1.394 1.496 0.761 0.761 1.860 0.761 0.761 0.761 1.247
## [19631] 0.761 1.814 1.482 0.761 0.761 0.762 1.442 1.412 0.761 0.762
## [19641] 0.761 1.517 0.762 0.762 0.761 1.237 2.142 1.439 0.762 0.761
## [19651] 0.761 1.151 0.761 1.457 0.761 0.762 0.762 0.762 0.761 0.761
## [19661] 0.762 1.133 0.761 0.761 0.762 1.009 0.761 0.761 1.093 0.761
## [19671] 0.761 0.853 0.761 0.761 0.762 1.181 0.761 0.762 0.762 0.761
## [19681] 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [19691] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761
## [19701] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [19711] 0.762 0.761 0.761 0.761 0.762 1.042 0.761 0.761 0.762 0.761
## [19721] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [19731] 0.762 0.761 0.761 0.762 0.762 1.462 0.761 0.761 0.762 0.761
## [19741] 1.351 0.761 0.761 0.761 0.905 0.761 0.761 0.761 0.761 1.487
## [19751] 0.761 1.464 0.761 0.761 1.217 1.341 1.513 1.527 1.479 0.761
## [19761] 1.230 0.762 1.293 1.362 1.315 1.411 0.761 0.762 1.383 1.398
## [19771] 1.090 1.400 1.186 0.761 1.454 0.761 1.353 1.474 0.761 1.059
## [19781] 1.374 0.761 0.761 0.761 1.411 0.761 0.761 0.761 0.761 0.761
## [19791] 0.761 0.761 1.502 0.761 0.761 1.464 0.761 0.761 0.761 0.761
## [19801] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.411
## [19811] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [19821] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762
## [19831] 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.762 0.762
## [19841] 0.761 0.761 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [19851] 0.761 0.761 0.761 0.762 0.761 0.761 1.497 0.762 0.761 0.761
## [19861] 1.374 0.761 0.761 1.993 0.761 0.761 0.762 0.900 0.761 0.761
## [19871] 0.762 1.419 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.762
## [19881] 0.761 0.761 1.118 0.762 0.761 0.761 0.762 0.762 0.761 0.761
## [19891] 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.762
## [19901] 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761
## [19911] 0.762 1.258 0.761 0.761 1.504 1.429 0.761 0.761 1.511 0.761
## [19921] 1.476 0.761 0.762 1.390 0.762 0.761 0.761 1.133 1.316 0.761
## [19931] 0.888 0.848 0.761 1.257 0.762 1.117 1.334 0.762 0.762 0.762
## [19941] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [19951] 1.380 1.217 0.762 0.762 0.762 0.762 0.762 0.762 0.762 1.629
## [19961] 0.761 0.762 1.272 0.761 0.762 0.762 1.003 0.808 0.762 0.762
## [19971] 0.761 0.762 0.762 0.762 1.526 0.761 0.762 0.762 0.762 0.761
## [19981] 0.762 0.762 1.411 0.762 0.761 0.762 0.762 0.761 0.762 0.762
## [19991] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [20001] 1.216 0.761 0.762 0.762 1.323 0.761 0.762 0.762 1.343 0.762
## [20011] 0.762 0.762 0.761 1.464 1.503 0.761 1.396 0.762 1.225 1.524
## [20021] 1.467 0.762 0.761 1.521 1.411 1.523 0.761 1.790 0.761 1.452
## [20031] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.761
## [20041] 0.761 0.762 0.762 0.761 0.761 0.762 1.343 0.761 0.761 0.762
## [20051] 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761 0.761
## [20061] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20071] 0.761 0.761 0.761 0.836 0.768 0.868 0.764 0.762 0.762 0.771
## [20081] 0.761 0.762 0.778 0.762 0.762 0.782 0.762 0.778 0.762 0.798
## [20091] 0.762 0.828 0.830 0.816 0.762 0.779 0.762 0.802 0.821 0.761
## [20101] 0.804 0.761 0.824 0.761 0.815 0.761 0.794 0.761 0.782 0.761
## [20111] 0.783 0.761 0.784 0.761 0.787 0.761 0.797 0.761 0.791 0.761
## [20121] 0.798 0.761 0.790 0.761 0.789 0.761 0.767 0.761 0.761 0.761
## [20131] 0.761 0.761 0.761 0.837 0.761 0.767 0.761 0.764 0.761 0.787
## [20141] 0.780 0.782 0.788 0.765 0.779 0.770 0.792 0.773 0.780 0.765
## [20151] 0.777 0.767 0.779 0.765 0.777 0.765 0.778 0.767 0.773 0.766
## [20161] 0.772 0.772 0.774 0.766 0.772 0.771 0.794 0.774 0.778 0.766
## [20171] 0.773 0.762 0.775 0.762 0.778 0.762 0.792 0.795 0.761 0.761
## [20181] 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.761
## [20191] 0.761 0.761 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.761
## [20201] 0.761 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.762 0.761
## [20211] 1.032 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20221] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20231] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20241] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20251] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20261] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20271] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20281] 0.761 0.762 0.771 0.762 0.762 0.762 0.762 0.773 0.762 0.762
## [20291] 0.762 0.762 0.762 0.857 0.762 0.762 0.821 0.762 0.793 0.762
## [20301] 0.766 0.762 0.764 0.761 0.761 0.761 0.763 0.761 0.761 0.761
## [20311] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20321] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20331] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20341] 0.761 0.761 0.761 0.761 0.761 0.761 0.791 0.841 0.762 0.827
## [20351] 0.816 0.762 0.817 0.866 0.762 0.857 0.815 0.762 0.803 0.761
## [20361] 0.762 0.816 0.792 0.762 0.830 0.760 0.762 0.823 0.762 0.816
## [20371] 0.762 0.810 0.762 0.785 0.762 0.781 0.762 0.811 0.761 0.815
## [20381] 0.761 0.810 0.761 0.814 0.761 0.791 0.761 0.786 0.761 0.795
## [20391] 0.761 0.801 0.761 0.789 0.761 0.806 0.761 0.796 0.761 0.788
## [20401] 0.761 0.792 0.761 0.791 0.761 0.798 0.761 0.761 0.761 0.761
## [20411] 0.761 0.761 0.855 0.761 0.799 0.761 0.773 0.761 0.765 0.761
## [20421] 0.788 0.761 0.768 0.761 0.773 0.761 0.799 0.761 0.781 0.804
## [20431] 0.761 0.768 0.761 0.783 0.761 0.804 0.799 0.771 0.761 0.789
## [20441] 0.761 0.793 0.779 0.761 0.775 0.761 0.786 0.761 0.799 0.827
## [20451] 0.761 0.762 0.763 0.762 0.763 0.762 0.762 0.763 0.761 0.762
## [20461] 0.761 0.762 0.762 0.762 0.761 0.762 0.764 0.762 0.762 0.761
## [20471] 0.764 0.762 0.761 0.763 0.762 0.761 0.761 0.762 0.761 0.761
## [20481] 0.762 0.763 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.761
## [20491] 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.762
## [20501] 0.762 0.761 0.762 0.761 0.761 0.762 0.762 0.761 0.762 0.761
## [20511] 0.761 0.761 0.763 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20521] 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.781
## [20531] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20541] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.768
## [20551] 0.762 0.761 0.761 0.762 0.763 0.761 0.762 0.761 0.761 0.761
## [20561] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20571] 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.762
## [20581] 0.762 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.761 0.763
## [20591] 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762 0.762 0.762
## [20601] 0.762 0.762 0.762 0.762 0.762 0.762 0.761 0.761 0.761 0.761
## [20611] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20621] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20631] 0.762 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.762
## [20641] 0.761 0.761 0.762 0.761 0.761 0.762 0.761 0.760 0.762 0.761
## [20651] 0.761 0.762 0.761 0.762 0.761 0.761 0.762 0.761 0.761 0.762
## [20661] 0.762 0.761 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [20671] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20681] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20691] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20701] 0.823 0.879 0.934 0.910 0.975 0.874 0.994 1.192 1.321 0.876
## [20711] 1.038 0.953 0.761 0.912 0.761 0.834 0.761 0.817 0.925 0.859
## [20721] 1.156 1.589 1.320 1.063 0.778 0.777 0.777 0.781 0.782 0.786
## [20731] 0.761 1.191 1.054 1.080 1.053 0.963 0.912 0.964 1.086 1.111
## [20741] 0.868 1.135 0.770 1.059 0.879 0.797 0.869 1.038 0.786 0.869
## [20751] 1.038 0.786 1.084 0.905 0.987 1.055 1.109 1.047 0.765 0.761
## [20761] 0.761 0.761 0.761 0.761 0.761 0.762 1.116 1.479 0.890 1.234
## [20771] 1.203 0.839 0.761 0.762 0.761 0.761 0.762 0.762 0.762 0.761
## [20781] 0.761 1.936 1.062 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20791] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.197
## [20801] 1.925 1.316 0.769 0.761 0.761 0.771 0.762 0.761 0.761 0.761
## [20811] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20821] 0.761 0.761 0.761 0.762 0.762 0.762 0.853 0.762 0.762 0.825
## [20831] 0.831 0.815 0.823 0.792 0.771 0.761 0.761 0.763 0.764 1.960
## [20841] 0.761 0.761 0.761 0.761 1.026 2.246 0.761 0.980 1.027 0.761
## [20851] 0.761 1.022 0.761 0.808 0.882 0.987 0.887 0.802 0.914 0.761
## [20861] 1.041 2.264 1.097 1.104 0.930 2.437 0.932 0.904 1.239 0.761
## [20871] 1.154 0.761 0.764 0.936 0.921 0.762 0.761 0.761 0.761 0.761
## [20881] 0.761 0.761 0.761 0.765 0.765 0.765 0.765 0.765 0.761 0.846
## [20891] 0.845 0.809 0.802 0.801 1.149 1.897 0.761 1.030 1.114 0.870
## [20901] 0.888 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.830
## [20911] 1.195 1.058 0.888 0.792 0.763 0.761 0.761 0.762 0.779 0.761
## [20921] 0.761 0.760 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20931] 0.762 0.761 1.025 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [20941] 0.761 0.761 0.761 0.765 0.765 0.933 0.828 0.824 0.786 0.761
## [20951] 0.761 0.761 0.761 0.761 0.761 0.905 1.215 2.211 1.042 1.600
## [20961] 2.753 0.761 1.033 0.823 0.761 0.761 1.969 1.072 1.040 0.969
## [20971] 0.890 0.775 0.970 1.309 1.190 0.761 1.310 1.317 0.761 0.968
## [20981] 1.060 0.761 0.761 1.312 1.298 1.196 0.762 0.939 1.206 1.045
## [20991] 1.150 0.971 1.044 1.167 0.782 1.312 1.366 0.761 2.554 1.397
## [21001] 1.087 1.370 1.199 1.383 0.795 1.155 1.400 1.401 1.423 1.226
## [21011] 1.380 1.366 1.397 1.407 1.415 1.420 1.413 1.315 1.093 1.048
## [21021] 1.097 1.073 1.086 1.113 1.075 1.146 1.116 1.089 1.094 1.131
## [21031] 1.131 1.078 1.157 1.166 1.047 1.083 1.089 1.104 1.171 1.175
## [21041] 1.268 1.400 1.095 1.084 1.084 0.927 0.912 1.122 1.114 1.071
## [21051] 1.068 1.069 1.044 1.039 1.032 1.065 0.819 0.879 0.922 0.927
## [21061] 1.054 0.799 0.919 0.937 0.793 0.891 0.800 0.843 0.907 0.882
## [21071] 0.881 0.926 0.856 0.812 0.777 0.790 0.763 0.767 0.788 0.779
## [21081] 0.764 0.761 0.846 0.790 0.795 0.791 0.830 0.761 0.880 0.846
## [21091] 0.927 0.987 0.938 0.835 0.904 0.812 0.873 0.815 0.876 0.837
## [21101] 0.761 0.945 0.978 0.863 0.872 0.866 0.917 0.868 0.936 0.864
## [21111] 0.910 0.948 0.858 0.837 0.922 0.871 0.795 0.845 0.832 0.785
## [21121] 0.798 0.781 0.761 0.766 0.761 0.790 0.761 0.801 0.761 0.781
## [21131] 0.779 0.770 0.784 0.858 0.774 0.761 0.773 0.761 0.842 0.921
## [21141] 0.761 0.794 0.783 0.878 0.761 0.766 0.827 0.777 0.761 0.796
## [21151] 0.761 0.778 0.761 0.761 0.761 0.781 0.761 0.762 0.761 0.782
## [21161] 0.803 0.762 0.779 0.807 0.836 0.783 1.132 0.809 0.773 0.803
## [21171] 0.890 0.791 1.145 0.763 0.989 0.778 0.761 0.761 0.762 0.762
## [21181] 0.761 1.315 1.081 1.091 1.091 1.080 1.073 1.092 1.083 1.067
## [21191] 1.083 1.082 1.012 1.085 1.078 1.081 1.145 1.102 1.085 1.080
## [21201] 1.075 1.059 1.070 1.082 1.068 1.351 1.199 1.230 1.206 1.183
## [21211] 1.088 1.182 1.076 1.037 0.940 0.874 0.829 1.077 1.370 1.101
## [21221] 1.056 1.077 1.055 1.121 1.131 0.828 0.816 1.214 1.122 1.092
## [21231] 1.065 1.073 1.062 1.076 1.075 1.084 0.843 0.765 0.762 0.761
## [21241] 0.761 0.761 0.761 0.761 0.761 0.765 0.762 0.790 0.805 0.761
## [21251] 0.761 0.810 0.761 0.801 0.798 0.802 0.761 0.805 0.761 0.761
## [21261] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [21271] 0.761 0.761 0.797 0.761 0.855 0.874 0.819 0.817 0.762 0.899
## [21281] 0.921 0.762 0.791 0.762 0.763 0.762 0.911 0.764 0.816 0.762
## [21291] 0.783 0.780 0.784 0.838 0.784 0.768 0.791 0.761 0.777 0.761
## [21301] 0.761 0.764 0.767 0.878 0.789 0.807 0.761 0.806 0.821 0.763
## [21311] 0.808 0.791 0.761 0.850 0.761 0.912 0.953 0.857 0.870 0.782
## [21321] 0.794 0.763 0.763 0.765 0.763 0.869 0.998 0.761 1.514 1.166
## [21331] 1.103 0.761 0.991 0.827 0.811 0.968 1.071 0.896 0.791 0.791
## [21341] 0.799 0.799 0.983 0.763 0.763 1.064 1.172 0.762 0.762 1.154
## [21351] 0.762 0.762 1.145 0.762 0.762 1.221 0.762 1.028 0.926 1.094
## [21361] 0.778 1.232 0.987 1.093 1.056 1.280 1.041 0.762 1.124 1.108
## [21371] 0.995 1.179 1.121 1.184 1.142 1.203 0.761 1.214 1.191 0.761
## [21381] 1.347 0.761 0.761 0.761 1.143 0.761 1.071 1.058 1.125 1.011
## [21391] 1.235 1.188 1.022 0.831 1.354 1.041 1.140 1.434 1.333 1.435
## [21401] 0.923 0.762 0.916 0.762 0.925 0.762 0.893 0.762 0.762 0.762
## [21411] 0.879 0.762 0.762 0.762 0.885 0.762 0.762 0.762 0.832 0.762
## [21421] 0.762 0.762 0.761 0.762 0.762 0.761 0.761 0.761 0.762 0.762
## [21431] 0.842 0.763 0.762 0.762 0.761 0.761 0.762 0.762 0.761 0.761
## [21441] 0.762 0.768 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.868
## [21451] 0.761 0.761 0.762 0.762 0.761 0.761 0.762 0.761 0.761 0.761
## [21461] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.777
## [21471] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761
## [21481] 0.762 0.976 0.761 0.761 0.762 0.824 0.761 0.761 0.762 0.761
## [21491] 0.761 0.761 0.762 0.761 0.761 0.761 0.762 1.050 0.761 0.761
## [21501] 0.762 0.914 0.761 0.761 0.762 0.762 0.761 0.761 0.803 0.762
## [21511] 0.761 0.761 0.762 0.761 0.761 1.030 0.777 0.761 0.761 0.761
## [21521] 0.762 0.761 0.761 0.859 0.761 0.761 0.761 0.925 0.766 0.761
## [21531] 0.761 0.805 0.788 0.952 0.761 0.761 0.761 0.762 0.830 0.761
## [21541] 0.930 0.761 0.762 0.861 0.761 1.046 0.761 0.762 0.838 0.761
## [21551] 1.029 0.761 0.762 0.900 0.761 1.067 0.762 0.762 0.813 0.761
## [21561] 1.032 0.761 0.852 0.761 1.029 0.761 0.860 0.761 0.812 0.762
## [21571] 0.855 0.761 0.826 0.762 0.851 0.762 0.783 0.762 0.762 0.808
## [21581] 0.761 0.761 0.762 0.762 0.767 0.762 0.761 0.762 0.763 0.761
## [21591] 0.767 0.762 0.763 0.791 0.761 0.762 0.787 0.761 0.762 0.761
## [21601] 0.761 0.762 0.786 0.761 0.761 0.762 0.762 0.761 0.761 0.762
## [21611] 0.761 0.761 0.761 0.762 0.760 0.761 0.761 0.762 0.761 0.761
## [21621] 0.761 0.762 0.769 0.761 0.761 0.762 0.761 0.761 0.761 0.762
## [21631] 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.762 1.421
## [21641] 0.761 1.258 0.761 0.761 1.062 0.761 0.761 1.560 0.761 0.761
## [21651] 0.883 0.761 0.761 1.490 0.761 0.761 1.740 0.761 0.761 1.594
## [21661] 0.761 0.761 0.914 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [21671] 0.761 0.761 0.761 1.139 1.160 0.761 0.761 1.251 0.761 0.761
## [21681] 1.320 0.761 0.761 1.086 0.873 0.761 0.761 0.761 0.761 1.480
## [21691] 0.761 0.761 1.173 0.761 0.761 0.965 0.856 0.761 1.231 0.839
## [21701] 0.761 1.138 0.761 0.761 1.713 0.761 0.821 1.477 1.220 0.761
## [21711] 1.239 0.761 0.761 1.096 0.761 0.761 0.968 0.761 0.761 1.116
## [21721] 0.761 0.761 0.960 0.761 0.761 0.905 0.761 0.761 0.762 0.761
## [21731] 0.761 0.928 0.761 0.761 0.762 0.815 0.762 0.761 0.761 0.762
## [21741] 0.761 0.761 0.762 0.761 0.762 0.761 0.762 0.763 0.761 1.044
## [21751] 0.762 0.761 1.015 0.762 0.761 1.062 0.761 0.761 0.772 0.927
## [21761] 1.053 1.366 0.761 1.089 0.761 0.761 0.856 0.761 1.385 0.761
## [21771] 0.976 0.761 0.761 0.980 0.761 0.761 0.761 0.761 1.039 0.761
## [21781] 0.761 0.966 0.761 0.761 1.382 0.761 0.761 0.764 0.763 0.769
## [21791] 0.768 0.767 0.763 0.768 0.767 0.763 0.766 0.767 0.768 0.765
## [21801] 0.768 0.769 0.764 0.762 0.764 0.769 0.775 0.777 0.774 0.764
## [21811] 0.763 0.763 0.765 0.776 0.768 0.766 0.766 0.769 0.767 0.771
## [21821] 0.769 0.771 0.774 0.783 0.771 0.777 0.771 0.770 0.773 0.769
## [21831] 0.768 0.771 0.768 0.782 0.769 0.768 0.770 0.771 0.770 0.767
## [21841] 0.933 0.984 0.767 0.766 0.770 0.771 0.767 0.771 0.812 0.772
## [21851] 0.774 0.776 0.772 0.771 0.772 0.772 0.780 0.774 0.772 0.777
## [21861] 0.775 0.779 0.774 0.784 0.784 0.782 0.778 0.790 0.786 0.795
## [21871] 0.804 0.787 0.774 0.766 0.767 0.767 0.768 0.768 0.768 0.766
## [21881] 0.783 0.882 0.942 0.937 0.879 0.880 0.868 0.810 0.885 0.810
## [21891] 0.895 0.858 0.859 0.861 0.841 0.856 0.851 0.802 0.827 0.861
## [21901] 0.878 0.819 0.849 0.837 0.805 0.822 0.804 0.806 0.824 0.856
## [21911] 0.851 0.809 0.801 0.841 4.832 0.814 1.640 3.098 2.500 1.902
## [21921] 1.418 1.149 3.463 3.322 2.462 4.689 2.104 3.951 1.163 1.916
## [21931] 5.188 9.283 5.285 7.480 5.268 4.734 2.401 1.368 6.784 7.548
## [21941] 8.372 7.651 3.549 2.562 3.470 4.713 3.940 4.456 2.341 0.800
## [21951] 1.035 1.765 2.084 0.761 2.041 2.859 1.486 1.236 1.462 2.585
## [21961] 2.550 1.069 1.470 2.396 1.507 2.935 2.560 1.376 2.506 2.977
## [21971] 3.008 2.307 2.109 1.194 1.323 2.921 2.733 1.399 1.146 0.761
## [21981] 0.762 0.762 0.762 0.761 0.773 4.751 0.840 1.325 1.318 3.289
## [21991] 1.365 2.159 1.689 1.339 1.366 1.519 0.761 1.947 0.761 3.015
## [22001] 1.344 1.915 1.304 1.377 2.112 1.964 2.690 2.701 1.327 2.051
## [22011] 2.335 1.637 1.938 1.819 2.169 1.678 2.958 2.479 2.556 2.553
## [22021] 1.994 2.595 2.454 2.907 3.825 3.501 3.662 2.434 1.626 2.087
## [22031] 1.935 1.374 1.299 1.316 1.357 1.370 2.410 11.112 1.002 1.747
## [22041] 0.987 3.289 0.761 0.766 0.769 0.767 0.761 1.641 0.764 0.779
## [22051] 0.761 0.777 0.761 0.799 0.761 0.865 0.761 0.776 0.791 0.812
## [22061] 0.796 0.804 0.769 0.787 0.762 0.778 0.761 0.768 0.761 0.773
## [22071] 0.761 0.761 0.762 0.763 0.761 0.777 0.761 0.772 0.761 0.770
## [22081] 0.762 0.770 0.890 0.767 0.767 0.777 0.789 0.791 0.771 0.776
## [22091] 0.801 0.789 0.761 0.790 0.766 0.770 0.766 1.106 0.763 0.761
## [22101] 0.761 0.761 0.761 0.762 0.761 0.762 0.942 0.808 0.818 0.781
## [22111] 0.774 0.770 0.763 0.762 0.762 3.364 2.305 3.743 1.334 0.866
## [22121] 1.242 2.620 1.318 1.350 4.056 14.143 0.761 0.764 0.761 0.761
## [22131] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [22141] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.762
## [22151] 0.761 0.761 0.763 0.761 0.761 0.761 0.762 0.765 0.763 0.761
## [22161] 0.763 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761
## [22171] 0.762 0.761 0.761 0.762 0.761 0.768 0.761 0.762 0.761 0.762
## [22181] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.831 0.761 0.761
## [22191] 0.763 0.762 0.761 0.762 0.761 0.761 0.761 0.762 0.762 0.761
## [22201] 0.761 0.761 0.762 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [22211] 0.763 0.761 0.761 0.953 1.248 2.270 0.880 1.280 0.948 0.852
## [22221] 0.866 0.927 1.219 1.140 0.906 1.693 1.397 1.496 0.762 1.798
## [22231] 1.639 1.969 1.273 0.820 0.764 2.286 1.545 1.317 1.652 1.032
## [22241] 1.025 1.609 1.419 1.202 1.556 1.224 1.320 1.372 1.235 1.312
## [22251] 1.385 1.361 1.240 1.220 1.047 1.276 0.938 1.115 0.819 0.974
## [22261] 0.852 0.809 1.353 1.047 1.769 1.037 1.388 1.204 1.385 1.217
## [22271] 1.325 1.480 1.363 1.205 1.214 1.068 1.127 1.119 1.487 1.195
## [22281] 1.326 2.230 1.216 1.947 1.274 1.242 2.661 1.053 1.731 1.006
## [22291] 1.310 0.832 1.312 1.121 1.133 1.061 1.260 3.824 2.356 3.138
## [22301] 1.570 0.761 1.922 1.802 1.903 2.543 2.018 1.419 2.163 1.358
## [22311] 2.507 1.373 3.497 1.185 1.775 1.499 1.695 1.320 1.497 3.223
## [22321] 1.708 1.482 1.584 0.981 1.815 1.280 1.262 1.195 1.629 1.667
## [22331] 1.850 1.311 2.221 1.473 2.281 1.585 1.937 1.903 1.816 2.011
## [22341] 1.570 1.525 1.626 2.030 2.222 2.025 1.451 1.655 1.620 1.811
## [22351] 1.371 1.828 1.625 2.229 1.459 1.804 1.342 1.567 1.767 1.910
## [22361] 1.253 1.631 2.058 2.415 2.496 2.582 2.549 3.221 3.198 2.659
## [22371] 2.393 6.273 0.884 2.560 2.291 1.259 3.591 1.812 1.520 1.245
## [22381] 1.408 1.345 1.548 1.873 2.307 1.937 1.594 1.934 0.768 0.780
## [22391] 0.796 0.771 0.773 0.832 0.775 0.793 0.793 0.818 1.284 1.080
## [22401] 0.853 0.797 0.771 0.812 0.792 0.768 2.646 0.764 0.789 0.769
## [22411] 3.347 0.761 0.761 0.763 0.913 0.761 0.761 0.762 0.878 0.930
## [22421] 0.878 0.904 0.938 0.854 0.895 0.761 0.761 0.901 0.869 0.907
## [22431] 0.944 1.015 1.062 1.211 0.907 1.055 0.927 0.862 0.801 0.772
## [22441] 0.772 1.141 2.052 1.327 1.064 1.251 0.766 0.827 0.761 0.762
## [22451] 0.760 2.665 3.201 2.609 1.484 1.464 2.043 1.729 2.165 2.171
## [22461] 1.141 1.074 1.231 0.968 2.022 2.569 1.199 2.163 1.209 0.986
## [22471] 1.159 2.233 2.049 1.927 1.375 1.560 2.208 1.339 4.605 1.439
## [22481] 1.120 3.304 2.901 1.943 1.630 1.489 2.601 1.295 1.725 1.722
## [22491] 1.785 2.368 1.868 1.323 1.362 1.264 2.747 1.675 1.329 1.632
## [22501] 0.761 0.761 0.761 1.167 1.943 2.052 1.395 0.874 1.154 1.700
## [22511] 2.483 2.226 1.354 0.889 1.167 0.941 1.527 1.585 0.929 1.610
## [22521] 1.404 1.294 1.506 1.701 7.170 1.222 1.182 2.647 3.872 5.113
## [22531] 2.936 4.079 1.434 1.158 0.875 1.524 0.943 1.330 0.919 1.030
## [22541] 2.179 1.704 1.172 1.351 0.960 2.477 1.868 2.635 0.761 0.761
## [22551] 0.761 0.762 0.761 0.761 0.761 0.761 1.257 1.243 1.318 1.124
## [22561] 1.224 0.940 0.762 0.761 1.300 1.407 1.817 1.688 1.554 1.488
## [22571] 0.929 1.016 1.065 1.289 1.486 1.477 1.004 1.960 1.107 0.776
## [22581] 2.145 1.354 1.625 0.762 1.211 1.206 2.212 2.686 1.407 2.038
## [22591] 1.256 1.239 1.133 0.761 1.048 1.677 1.146 0.764 1.274 0.903
## [22601] 1.073 1.060 1.276 0.875 1.372 0.762 1.291 1.245 1.372 1.819
## [22611] 0.990 1.383 1.400 0.933 1.184 1.178 1.230 0.814 1.016 0.803
## [22621] 0.799 0.914 1.059 1.200 1.418 1.663 1.644 1.790 1.085 1.604
## [22631] 1.350 1.638 1.123 1.543 1.185 1.434 1.544 1.416 1.148 2.137
## [22641] 2.010 1.326 2.391 2.464 1.999 1.863 1.625 1.840 2.308 1.849
## [22651] 1.206 1.309 1.041 1.312 0.966 1.739 1.033 1.572 1.662 1.378
## [22661] 0.786 1.042 0.969 1.073 0.895 1.142 1.165 1.464 1.100 0.800
## [22671] 1.437 1.410 0.897 2.171 1.701 1.303 0.863 0.894 1.306 3.566
## [22681] 1.301 0.766 0.842 1.082 1.069 1.273 0.808 1.528 5.018 0.761
## [22691] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [22701] 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.615
## [22711] 2.881 0.761 0.763 0.761 0.761 0.761 0.766 0.763 0.761 0.761
## [22721] 0.761 0.761 0.761 2.006 0.761 0.768 0.791 0.842 1.543 1.873
## [22731] 1.404 1.584 1.869 1.551 1.422 1.890 1.374 0.993 1.864 2.140
## [22741] 1.684 2.259 1.885 2.351 2.108 2.487 1.692 2.155 1.258 2.126
## [22751] 1.061 3.545 3.784 2.508 1.966 1.736 1.164 1.480 1.662 1.645
## [22761] 1.695 1.880 1.833 1.907 2.437 2.673 2.132 1.843 2.303 2.015
## [22771] 2.051 2.386 1.955 1.895 1.676 1.838 1.832 1.681 0.955 2.230
## [22781] 1.637 2.149 2.604 2.135 1.092 2.264 1.188 1.789 1.297 1.564
## [22791] 1.743 1.635 1.800 1.553 1.822 1.583 1.768 1.494 1.423 1.892
## [22801] 1.367 1.560 1.389 1.563 1.863 2.135 1.551 1.852 1.513 1.532
## [22811] 2.282 1.900 1.922 2.072 2.015 1.955 1.938 2.179 2.046 2.070
## [22821] 1.584 1.334 2.151 2.185 1.751 2.105 2.263 2.123 2.071 2.054
## [22831] 2.187 1.757 1.072 1.345 1.302 1.461 1.445 1.095 0.798 1.017
## [22841] 1.726 1.833 1.823 1.990 1.925 1.928 1.651 1.603 1.608 1.625
## [22851] 1.837 1.771 1.332 2.977 1.021 0.927 1.044 1.369 1.007 1.177
## [22861] 1.234 0.892 1.361 1.429 1.055 1.127 1.133 1.084 1.131 0.978
## [22871] 1.299 1.274 1.319 1.237 1.208 1.069 1.348 1.409 2.249 1.384
## [22881] 3.306 1.449 1.281 1.432 1.515 2.323 1.250 1.389 1.768 1.548
## [22891] 0.917 1.024 1.011 1.170 1.015 0.941 1.283 1.140 1.371 1.093
## [22901] 1.157 1.228 1.043 1.128 1.251 1.208 1.021 1.027 1.150 1.174
## [22911] 1.396 1.297 1.617 1.313 2.206 1.021 1.871 1.490 1.829 1.462
## [22921] 2.283 1.496 2.336 1.478 1.640 1.361 1.784 1.408 1.349 1.282
## [22931] 1.301 1.155 1.378 1.468 1.440 1.446 1.454 1.462 1.437 1.416
## [22941] 1.426 1.652 2.027 2.280 2.356 2.029 2.218 2.260 1.471 0.985
## [22951] 2.626 1.424 2.313 3.677 2.748 1.906 2.214 2.158 2.021 1.419
## [22961] 1.933 1.721 1.683 2.127 1.298 1.782 2.056 1.118 1.353 2.055
## [22971] 1.362 1.088 1.401 1.006 1.526 1.196 1.714 1.033 1.628 1.412
## [22981] 1.281 1.004 1.395 2.818 1.305 2.479 1.282 1.795 1.662 2.090
## [22991] 0.761 2.766 0.761 2.030 1.131 1.897 0.892 1.476 2.065 1.403
## [23001] 1.590 2.632 1.992 2.011 1.586 1.307 2.409 1.927 1.580 1.597
## [23011] 1.870 1.495 1.466 1.572 1.510 1.582 1.452 2.296 1.712 1.656
## [23021] 1.657 1.515 1.556 1.375 1.714 1.906 1.842 1.486 1.385 1.459
## [23031] 1.454 1.436 1.907 1.367 2.175 2.606 2.086 1.326 1.848 1.157
## [23041] 1.463 1.100 1.330 0.934 1.264 1.410 1.484 1.266 1.035 1.267
## [23051] 1.167 1.195 1.393 1.343 1.487 1.505 1.433 1.397 1.527 0.761
## [23061] 1.396 0.993 1.395 1.526 1.527 1.183 1.260 1.195 0.762 1.903
## [23071] 1.284 1.306 0.761 1.518 1.785 1.428 1.515 0.761 1.491 1.526
## [23081] 1.912 1.604 2.878 1.515 1.880 1.508 1.884 1.470 1.525 1.514
## [23091] 1.522 1.413 2.170 1.790 1.753 1.593 1.387 1.295 1.527 1.463
## [23101] 0.761 0.761 1.096 1.052 0.761 1.465 1.523 1.504 1.500 1.520
## [23111] 0.761 1.453 0.761 0.762 1.985 2.194 1.414 2.162 0.775 1.708
## [23121] 0.863 0.838 1.123 0.905 1.329 0.986 0.977 0.761 0.761 0.763
## [23131] 1.370 1.438 0.761 0.761 0.762 0.763 1.035 0.761 0.761 1.109
## [23141] 0.761 0.763 0.762 0.761 1.475 0.762 0.762 0.761 1.503 0.762
## [23151] 0.762 0.761 0.762 1.469 1.477 0.761 1.341 1.275 1.982 0.761
## [23161] 1.474 0.762 0.762 0.761 0.762 1.462 0.761 0.762 0.762 1.104
## [23171] 0.761 0.762 1.387 1.456 1.244 0.762 0.762 1.337 0.762 0.762
## [23181] 0.762 1.995 0.762 0.762 1.192 1.442 0.762 0.762 0.761 0.762
## [23191] 0.762 0.762 0.761 0.762 1.186 0.762 0.761 1.103 1.528 0.762
## [23201] 0.761 1.470 0.762 0.762 0.761 0.762 0.762 0.762 0.923 1.355
## [23211] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [23221] 0.761 0.762 0.762 0.762 0.761 0.762 0.998 0.762 0.761 0.762
## [23231] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [23241] 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [23251] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [23261] 0.761 1.424 0.762 0.761 0.762 0.762 2.050 0.761 0.762 0.762
## [23271] 0.762 1.424 0.762 0.762 2.282 0.762 0.762 0.762 1.264 0.762
## [23281] 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761
## [23291] 0.762 0.762 0.761 0.762 0.762 0.762 1.347 0.762 0.762 0.762
## [23301] 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [23311] 0.762 0.762 1.438 0.762 0.762 0.762 1.153 0.762 0.762 0.762
## [23321] 0.761 0.762 0.762 0.761 0.761 0.762 1.180 0.762 0.761 0.762
## [23331] 0.762 0.762 0.761 0.762 0.762 0.762 0.761 0.762 0.762 0.762
## [23341] 0.761 1.260 0.762 0.762 0.761 0.762 0.762 0.761 0.762 0.761
## [23351] 0.762 0.762 0.761 0.762 0.762 0.761 0.762 0.762 0.761 0.762
## [23361] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.762 0.761 0.761
## [23371] 0.762 0.761 0.761 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [23381] 0.762 1.119 0.762 0.762 1.374 0.762 1.146 1.089 1.924 0.762
## [23391] 0.762 2.134 0.762 0.762 0.762 0.762 0.892 0.762 0.762 0.762
## [23401] 0.762 0.764 0.762 0.762 0.762 0.762 1.189 0.761 0.761 1.603
## [23411] 0.762 0.762 2.100 1.518 2.212 1.527 1.506 1.177 1.410 0.761
## [23421] 0.762 0.762 0.762 0.762 1.524 1.418 2.244 0.762 0.762 0.762
## [23431] 0.762 0.761 1.686 1.632 0.762 2.230 1.526 0.761 1.746 1.527
## [23441] 2.283 1.162 1.322 1.343 1.327 1.036 0.761 0.762 1.336 0.771
## [23451] 2.161 1.964 1.768 1.289 1.553 0.762 0.762 1.178 1.438 0.761
## [23461] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.897 1.419
## [23471] 0.761 0.761 1.521 1.397 0.761 0.761 1.140 1.449 0.762 0.761
## [23481] 1.505 1.467 0.761 1.250 1.527 0.761 0.762 1.515 1.180 0.762
## [23491] 0.762 1.511 1.267 0.762 0.762 1.419 1.514 0.762 0.761 1.490
## [23501] 1.517 0.762 0.762 1.073 1.039 0.762 0.762 0.896 0.762 0.762
## [23511] 0.762 0.761 0.899 0.761 0.762 0.823 0.761 0.761 0.762 1.321
## [23521] 0.890 0.761 0.761 1.170 1.491 0.761 0.761 0.761 1.513 0.761
## [23531] 0.762 0.761 1.517 0.762 0.762 1.095 1.524 0.761 0.762 1.520
## [23541] 1.386 0.761 0.762 1.514 1.527 0.761 0.762 1.501 1.488 0.761
## [23551] 0.762 1.526 1.453 0.761 1.411 1.099 0.761 0.761 1.657 1.453
## [23561] 0.761 0.761 1.507 0.874 0.761 0.761 1.287 1.117 0.761 0.761
## [23571] 1.463 1.090 0.761 0.761 1.893 1.472 0.761 0.761 2.134 1.525
## [23581] 0.761 0.761 0.761 1.482 0.761 0.761 1.524 1.863 0.761 0.761
## [23591] 1.527 1.293 0.761 0.761 1.298 1.517 0.761 0.761 1.305 1.318
## [23601] 0.761 0.761 1.453 0.761 0.761 1.511 0.761 0.761 0.761 0.761
## [23611] 0.761 0.761 0.761 1.431 0.761 0.761 0.761 0.761 0.761 1.936
## [23621] 0.761 0.761 0.761 1.480 0.761 1.524 0.762 0.761 0.761 1.346
## [23631] 1.137 0.761 0.761 1.475 0.761 0.761 1.092 0.761 1.482 0.869
## [23641] 0.761 0.761 0.861 0.761 0.763 0.761 0.761 1.740 0.761 0.761
## [23651] 1.192 0.842 1.322 0.868 0.761 0.761 1.109 0.858 0.761 0.761
## [23661] 1.159 1.375 0.762 0.761 0.761 0.761 0.762 0.761 0.762 0.761
## [23671] 0.762 0.761 0.762 0.762 0.762 0.762 1.103 0.762 0.762 0.762
## [23681] 0.762 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.761
## [23691] 0.762 0.761 0.761 0.761 0.777 0.761 0.762 0.978 0.762 0.762
## [23701] 0.761 1.100 0.762 0.761 0.761 0.761 0.762 1.198 0.761 0.762
## [23711] 0.761 0.762 0.938 0.762 0.761 0.761 0.761 0.762 0.761 0.761
## [23721] 1.507 0.761 0.761 1.333 0.762 0.761 0.761 0.761 0.762 0.761
## [23731] 0.761 0.761 0.762 0.761 1.453 1.357 0.761 0.762 0.761 0.761
## [23741] 0.761 0.761 0.761 0.761 0.761 0.761 1.527 0.761 0.761 0.761
## [23751] 0.761 0.761 0.761 1.431 0.761 0.761 1.936 0.761 0.761 1.527
## [23761] 1.528 0.761 0.761 0.761 0.761 0.761 0.761 1.507 1.411 0.761
## [23771] 0.761 1.523 1.619 1.511 0.761 0.762 0.761 0.761 0.761 0.761
## [23781] 0.761 1.247 0.761 0.761 0.889 0.761 0.761 0.761 0.761 0.761
## [23791] 0.761 0.761 0.761 0.880 0.761 0.761 0.761 0.761 0.761 0.761
## [23801] 0.761 1.503 0.761 1.503 1.375 1.619 1.274 0.761 1.504 1.477
## [23811] 0.987 0.761 0.761 0.762 1.655 0.761 0.761 0.762 0.762 1.487
## [23821] 0.761 0.762 0.761 0.762 1.180 1.405 0.761 0.761 0.762 1.483
## [23831] 1.416 0.761 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [23841] 0.762 0.762 0.762 0.762 0.762 0.761 0.762 0.762 0.762 0.762
## [23851] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [23861] 0.762 1.207 0.762 0.762 0.762 0.762 1.147 0.761 0.762 0.762
## [23871] 1.360 0.761 0.761 0.762 0.762 0.762 0.761 0.761 1.374 0.761
## [23881] 2.198 0.761 0.761 0.762 0.761 0.761 0.761 0.762 1.311 1.509
## [23891] 1.341 0.762 1.410 1.112 0.761 0.761 0.762 0.761 0.761 0.761
## [23901] 1.414 1.526 0.761 0.762 0.762 0.762 0.895 1.381 0.762 0.762
## [23911] 0.762 1.518 0.762 0.762 0.762 1.358 0.762 0.762 0.762 0.762
## [23921] 1.459 0.762 0.762 0.762 1.424 1.445 0.762 0.762 0.762 0.762
## [23931] 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762 1.137
## [23941] 0.762 0.762 0.762 1.431 1.132 0.762 0.762 0.762 2.070 0.762
## [23951] 0.762 0.762 1.511 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [23961] 0.762 1.311 1.805 0.762 0.762 1.410 0.761 0.762 0.762 0.762
## [23971] 2.981 0.762 0.762 0.762 1.523 0.762 0.762 0.762 0.762 0.762
## [23981] 0.762 0.762 0.761 1.311 0.762 0.762 0.762 1.796 0.761 0.762
## [23991] 0.762 0.762 0.762 1.142 1.317 0.762 0.762 0.762 0.761 1.407
## [24001] 0.762 0.762 0.762 0.762 0.761 1.527 0.762 0.762 0.762 0.762
## [24011] 0.762 1.481 0.762 0.762 0.762 0.762 1.310 0.762 0.829 0.762
## [24021] 0.762 1.499 0.762 0.762 0.762 0.762 1.457 0.762 0.762 0.762
## [24031] 1.438 0.762 0.762 0.762 0.762 0.761 0.761 0.762 0.762 0.762
## [24041] 0.762 0.761 0.761 1.284 0.762 0.762 0.761 0.761 0.762 0.762
## [24051] 0.762 0.761 1.424 0.762 0.761 0.761 0.762 0.762 0.761 0.762
## [24061] 0.762 0.761 1.221 1.167 0.761 1.527 0.762 1.475 0.761 1.012
## [24071] 0.762 0.762 0.762 0.762 0.761 1.502 0.762 0.762 0.987 0.762
## [24081] 0.761 1.517 0.762 0.761 0.762 0.762 0.761 0.762 0.762 0.762
## [24091] 0.761 0.762 0.761 0.762 0.761 1.063 0.761 0.762 0.761 0.762
## [24101] 0.761 0.761 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.851
## [24111] 0.762 0.761 0.996 0.761 0.762 0.761 0.762 1.949 0.761 0.762
## [24121] 0.761 0.910 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761
## [24131] 1.109 0.761 1.422 0.761 0.828 0.761 0.761 0.761 0.761 0.761
## [24141] 0.761 0.761 0.761 0.761 1.496 0.761 0.761 1.084 0.761 0.761
## [24151] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [24161] 1.123 0.761 0.761 0.839 0.761 1.287 0.762 1.252 1.025 1.507
## [24171] 0.761 0.761 1.438 1.151 1.411 1.263 1.109 0.761 0.762 0.761
## [24181] 0.762 0.761 0.761 0.762 0.761 0.762 0.762 0.762 0.761 0.762
## [24191] 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.761 0.761 0.761
## [24201] 0.762 0.761 0.761 0.761 0.761 0.762 0.762 0.762 0.762 0.761
## [24211] 0.762 0.762 0.762 0.761 0.762 0.762 0.761 0.762 1.197 0.762
## [24221] 0.761 0.761 1.444 0.761 0.761 1.374 0.761 0.762 0.761 0.762
## [24231] 0.761 0.761 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.762
## [24241] 0.761 0.762 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [24251] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [24261] 2.122 1.527 0.761 1.137 0.761 0.761 1.517 0.761 0.762 0.761
## [24271] 0.761 1.411 0.761 0.761 0.761 1.923 0.761 2.027 0.762 0.761
## [24281] 0.762 0.762 0.762 0.762 0.762 0.762 1.184 0.762 0.761 0.761
## [24291] 0.762 1.973 0.762 0.762 0.762 0.762 0.762 0.762 0.762 0.762
## [24301] 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.761
## [24311] 0.761 0.762 0.761 0.762 0.761 0.761 0.762 1.522 0.761 0.762
## [24321] 0.762 0.761 0.762 0.761 0.762 0.761 0.761 0.761 0.761 0.762
## [24331] 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761 0.761
## [24341] 0.762 0.762 0.761 1.487 0.761 1.374 1.224 1.092 1.063 1.492
## [24351] 3.191 3.424 3.158 1.598 3.116 3.439 2.335 2.040 1.572 1.234
## [24361] 0.859 0.951 1.050 1.334 1.842 1.980 1.396 1.422 1.710 0.800
## [24371] 1.660 1.581 0.912 1.470 2.270 1.712 1.388 1.674 1.374 1.622
## [24381] 3.220 1.287 2.252 2.905 2.041 2.809 3.249 0.761 3.040 0.762
## [24391] 2.005 0.761 2.242 6.419 0.764 1.384 0.981 2.480 3.608 2.917
## [24401] 7.595 2.294 4.346 2.149 2.994 2.159 5.007 1.702 4.290 5.729
## [24411] 6.758 3.881 4.552 2.657 3.757 7.285 4.808 3.424 4.868 2.899
## [24421] 3.431 3.986 6.578 2.155 2.754 1.850 2.488 2.237 2.340 2.265
## [24431] 2.243 2.335 2.407 1.828 2.474 1.890 2.221 1.607 2.110 1.351
## [24441] 2.284 2.179 2.415 2.181 2.111 2.252 1.791 1.779 2.011 1.826
## [24451] 2.093 2.073 1.781 2.205 1.675 2.050 1.803 3.258 1.579 2.664
## [24461] 2.077 2.387 1.452 1.894 1.742 4.319 3.300 1.027 1.600 1.146
## [24471] 1.763 1.148 2.872 1.146 4.088 2.790 3.351 1.686 1.925 1.258
## [24481] 1.442 4.061 1.273 9.722 6.436 7.040 7.093 6.753 7.974 11.149
## [24491] 5.494 4.417 7.775 3.139 7.882 6.483 5.957 6.503 3.114 1.377
## [24501] 1.038 1.676 2.082 1.662 2.318 1.089 2.262 2.070 2.683 2.112
## [24511] 1.988 2.609 2.168 2.096 2.234 2.241 2.334 1.862 2.165 1.979
## [24521] 1.317 1.999 1.466 1.311 2.323 1.401 2.208 1.303 2.038 1.395
## [24531] 2.108 1.231 1.986 1.103 1.304 1.551 1.393 1.862 2.244 1.681
## [24541] 1.100 0.761 0.762 0.761 0.762 3.032 1.247 3.194 5.279 4.739
## [24551] 2.027 4.129 2.561 3.269 4.189 1.468 1.269 2.681 1.993 2.791
## [24561] 3.215 4.315 5.551 5.477 4.364 2.829 3.659 3.144 8.605 2.874
## [24571] 1.698 1.654 1.384 1.006 1.268 2.364 2.821 1.674 1.478 1.579
## [24581] 2.116 1.766 1.583 1.634 1.715 1.687 1.610 1.042 0.761 0.762
## [24591] 0.761 0.762 0.761 0.762 1.697 7.618 1.218 4.648 8.875 7.401
## [24601] 7.650 3.922 5.944 1.640 1.295 1.684 1.587 1.788 1.459 2.072
## [24611] 2.242 2.465 1.566 2.078 1.872 2.071 1.988 1.831 2.185 0.762
## [24621] 0.761 0.764 0.761 0.761 0.761 0.761 0.761 0.761 0.761 1.571
## [24631] 0.767 0.763 0.761 0.762 0.762 2.166 1.864 1.422 1.613 1.715
## [24641] 1.022 1.887 2.045 2.290 2.097 2.167 1.959 2.725 2.360 1.607
## [24651] 0.975 1.316 1.182 1.247 2.683 1.432 7.266 1.521 2.602 1.035
## [24661] 2.552 1.350 9.255 3.083 6.517 5.857 4.672 8.100 1.992 2.044
## [24671] 4.335 2.551 0.953 1.719 1.327 1.498 0.958 1.054 3.069 1.220
## [24681] 1.707 3.020 2.032 2.029 1.286 0.958 1.488 1.145 1.303 1.423
## [24691] 1.258 1.280 1.226 0.787 1.091 1.541 11.975 3.568 2.704 2.313
## [24701] 9.652 2.231 4.648 5.858 6.964 4.225 3.395 1.601 1.447 2.763
## [24711] 2.698 1.701 1.150 0.761 0.762 0.761 0.762 0.906 0.762 0.942
## [24721] 1.081 1.572 5.374 1.648 1.684 1.385 2.130 4.621 3.821 5.233
## [24731] 6.187 6.514 4.849 4.905 3.598 5.273 5.458 4.593 6.036 1.658
## [24741] 2.021 1.518 1.432 1.406 1.489 1.780 1.826 1.373 1.685 1.751
## [24751] 1.870 1.888 2.182 1.909 1.654 1.909 1.512 1.752 1.773 1.219
## [24761] 1.786 1.599 1.624 1.348 1.242 2.105 1.723 1.160 1.434 2.827
## [24771] 1.432 2.746 1.380 1.306 2.255 1.131 0.761 0.762 0.761 0.762
## [24781] 0.761 0.762 0.765 1.163 3.480 1.520 5.813 8.022 6.582 5.747
## [24791] 6.456 6.995 6.768 7.067 6.423 4.862 5.624 5.636 6.363 7.289
## [24801] 3.677 2.475 1.455 2.521 1.344 1.324 1.387 1.302 1.457 2.289
## [24811] 1.684 1.723 1.390 1.416 1.844 2.075 1.967 2.161 1.554 1.750
## [24821] 1.868 1.428 1.504 1.294 1.524 1.291 1.726 1.265 1.920 1.779
## [24831] 2.614 1.390 1.731 1.947 1.655 2.101 1.453 10.786 6.310 6.884
## [24841] 5.578 6.710 7.002 3.200 3.147 5.500 2.516 3.878 4.383 6.587
## [24851] 7.341 2.817 5.978 1.048 3.365 1.382 2.060 1.324 1.615 1.994
## [24861] 1.396 1.870 1.801 1.352 1.654 3.879 2.038 1.388 1.798 1.292
## [24871] 2.265 1.424 1.171 1.646 1.462 1.501 1.555 1.374 1.388 0.916
## [24881] 0.910 0.775 0.761 0.761 0.761 0.760 0.761 0.761 0.761 1.652
## [24891] 0.761 2.099 2.149 1.954 2.059 1.973 2.024 1.862 0.889 0.762
## [24901] 0.761 0.764 0.785 0.798 0.762 0.761 0.767 0.761 0.826 0.816
## [24911] 0.834 1.380 0.977 1.327 1.476 1.716 1.336 1.398 1.331 1.694
## [24921] 1.141 1.885 2.088 1.295 2.055 1.922 2.003 1.917 6.133 1.294
## [24931] 1.869 1.772 1.944 1.858 1.910 1.993 1.758 1.492 1.906 1.147
## [24941] 0.761 0.762 0.772 0.761 0.766 0.763 0.765 0.765 0.761 0.761
## [24951] 0.761 0.761 0.761 0.803 0.786 0.773 0.761 0.948 0.922 0.963
## [24961] 1.288 1.767 1.270 1.740 1.307 1.366 1.152 1.126 1.161 1.057
## [24971] 1.038 1.264 1.332 1.146 1.314 1.235 1.328 1.295 1.548 1.313
## [24981] 1.135 1.161 1.397 1.236 1.477 1.443 0.761 0.761 0.761 0.761
## [24991] 0.761 0.762 0.761 0.766 0.766 0.778 0.931 1.466 1.276 1.274
## [25001] 1.649 1.594 1.919 1.885 1.948 1.161 2.038 2.014 2.018 2.083
## [25011] 1.405 0.761 0.762 0.766 0.839 0.779 0.898 0.769 0.787 0.844
## [25021] 0.800 0.766 0.762 0.830 0.880 0.764 0.761 0.762 0.763 0.766
## [25031] 0.779 0.764 0.762 0.761 0.871 0.816 0.761 0.764 0.761 0.763
## [25041] 0.870 1.034 0.783 0.870 1.738 0.763 1.222 0.763 0.876 0.763
## [25051] 0.858 0.763 1.000 0.763 0.867 0.765 0.763 1.116 0.763 1.153
## [25061] 0.763 0.789 0.853 0.782 0.776 0.949 0.851 0.761 0.768 0.761
## [25071] 0.764 0.762 0.763 0.762 0.763 0.761 0.763 0.762 0.763 0.762
## [25081] 0.763 0.762 0.763 0.761 0.763 0.761 0.763 0.761 0.761 0.761
## [25091] 0.761 0.761 0.777 0.767 0.761 0.766 0.761 0.761 0.761 0.761
## [25101] 0.761 0.761 0.762 0.761 0.766 0.764 0.766 0.799 1.112 1.257
## [25111] 1.141 0.761 0.761 0.762 0.761 0.761 0.761 0.762 0.761 0.763
## [25121] 0.761 0.763 0.761 0.763 0.761 0.761 0.763 0.761 0.766 0.761
## [25131] 0.763 1.220 2.004 1.548 1.125 1.856 1.601 1.807 1.193 1.292
## [25141] 2.059 4.520 1.784 1.503 1.435 1.235 2.067 1.776 1.297 0.951
## [25151] 2.109 1.969 1.828 1.311 1.951 1.410 1.397 1.793 1.091 1.057
## [25161] 1.252 1.531 1.493 1.539 1.527 1.523 1.519 1.430 1.450 0.780
## [25171] 1.102 0.830 0.802 0.846 1.018 0.910 0.907 0.899 0.913 0.931
## [25181] 1.329 0.861 0.903 1.127 1.282 1.281 1.155 0.762 0.762 0.763
## [25191] 0.881 0.767 0.882 0.772 0.892 0.768 0.889 0.767 0.902 0.763
## [25201] 0.883 0.767 0.813 0.761 0.761 0.762 0.769 0.768 0.806 0.913
## [25211] 0.822 0.775 0.816 0.824 0.837 0.881 0.924 1.102 0.894 0.912
## [25221] 0.840 0.860 0.919 0.771 1.062 1.196 1.039 0.826 0.848 0.878
## [25231] 1.327 1.366 1.151 1.319 1.406 1.348 1.430 1.292 1.454 1.052
## [25241] 1.425 1.023 1.512 1.481 2.090 0.826 1.181 1.188 1.230 1.249
## [25251] 1.361 1.194 1.623 1.430 1.818 1.365 1.492 1.675 1.711 1.637
## [25261] 1.556 1.361 1.329 1.498 1.503 1.474 0.914 1.001 0.885 0.889
## [25271] 0.960 0.945 0.963 0.941 0.902 0.977 0.950 0.965 0.919 1.162
## [25281] 1.189 1.016 0.945 0.990 1.327 2.822 1.159 1.013 1.321 1.952
## [25291] 1.868 0.896 1.783 0.935 0.880 1.060 1.139 0.931 0.903 0.887
## [25301] 0.895 0.851 0.835 0.837 0.858 0.948 0.836 0.824 0.916 0.886
## [25311] 0.949 0.941 0.894 0.831 0.901 0.891 0.836 0.929 0.908 1.014
## [25321] 0.773 0.990 0.843 0.924 0.873 0.891 0.906 0.928 0.837 0.937
## [25331] 0.881 0.951 0.902 0.903 1.236 1.169 1.061 0.828 1.264 1.268
## [25341] 1.246 1.118 0.972 1.051 0.865 1.235 1.215 1.250 1.283 1.148
## [25351] 1.138 1.182 1.187 1.231 1.119 0.768 1.173 0.761 1.163 0.761
## [25361] 1.199 0.795 0.761 0.793 0.761 0.779 0.761 0.763 0.762 0.777
## [25371] 0.761 0.762 0.764 0.776 0.761 0.761 0.761 0.761 0.761 0.761
## [25381] 0.827 0.761 0.761 1.283 0.761 0.974 0.764 0.761 0.761 1.097
## [25391] 0.761 0.762 0.761 0.761 0.840 0.840 0.775 0.837 0.767 0.879
## [25401] 0.804 0.878 1.055 0.933 1.108 2.027 0.865 0.918 1.092 0.902
## [25411] 0.873 0.842 0.774 0.771 0.826 0.765 0.777 0.796 0.785 0.799
## [25421] 0.784 0.845 0.768 0.860 0.780 0.861 0.788 0.869 0.866 0.845
## [25431] 0.908 0.944 0.904 0.888 0.821 0.880 0.906 0.819 0.869 0.872
## [25441] 0.844 0.932 0.891 0.784 0.794 0.804 0.840 0.920 1.010 0.980
## [25451] 0.811 0.766 0.888 0.762 0.931 0.877 0.871 0.776 0.871 0.853
## [25461] 1.032 0.945 0.864 1.012 0.929 0.932 0.900 0.761 0.761 0.761
## [25471] 0.761 1.841 1.842 1.411 1.700 1.652 2.004 1.569 1.360 0.974
## [25481] 1.600 1.983 1.928 0.871 1.367 1.417 1.443 1.640 1.518 0.838
## [25491] 0.764 0.769 0.887 0.773 0.785 0.773 0.775 0.768 1.490 0.765
## [25501] 0.770 0.762 0.786 0.764 0.782 0.761 0.773 0.763 0.761 0.763
## [25511] 0.798 1.706 0.762 0.785 0.765 0.778 0.814 0.889 0.816 0.761
## [25521] 0.802 1.105 0.761 0.762 1.388 1.882 0.763 0.766 0.769 1.566
## [25531] 0.761 0.762 0.763 0.762 0.762 0.761 0.761 0.762 0.761 0.761
## [25541] 0.761 0.761 0.763 0.761 0.764 0.761 0.762 0.837 0.774 1.688
## [25551] 1.370 1.729 1.936 2.181 1.330 1.000 0.762 0.763 0.762 0.762
## [25561] 0.761 0.762 0.761 0.761 0.763 0.762 0.762 1.239 0.833 1.252
## [25571] 0.761 0.761 1.274 0.901 0.779 0.771 0.769 0.779 0.782 0.791
## [25581] 0.834 0.829 0.818 0.810 0.942 0.825 0.828 0.860 0.981 1.026
## [25591] 0.872 0.861 0.846 0.765 1.463 0.764 0.763 0.763 0.766 0.763
## [25601] 1.435 0.884 0.831 0.817 0.848 0.789 0.779 0.775 0.787 0.891
## [25611] 0.787 0.794getFitted(Final7) #predictions of the model for all points
x = getSimulations(Final7, nsim = 5, type = "refit") #extract simulations from the model
getRefit(Final7, x[[1]]) #model with simulated data## Formula:
## KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 |
## Transmitter) + (1 | File)
## Data: newData
## AIC BIC logLik df.resid
## 14582.352 14663.860 -7281.176 25602
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## Transmitter (Intercept) 0.2607
## File (Intercept) 0.1833
##
## Number of obs: 25612 / Conditional model: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0758
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) LengthStd Habitatdemersal
## -0.12127 0.27633 -0.23933
## Habitatpelagic-neritic ComImportmedium ComImportminor
## 0.25950 -0.06472 0.09535
## MonitArea_km2
## 0.02964getRefit(Final7, getObservedResponse(Final7)) #model with real data## Formula:
## KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 |
## Transmitter) + (1 | File)
## Data: newData
## AIC BIC logLik df.resid
## 9842.126 9923.634 -4911.063 25602
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## Transmitter (Intercept) 0.2656
## File (Intercept) 0.1745
##
## Number of obs: 25612 / Conditional model: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0753
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) LengthStd Habitatdemersal
## -0.17916 0.31695 -0.19042
## Habitatpelagic-neritic ComImportmedium ComImportminor
## 0.46219 -0.15810 -0.06784
## MonitArea_km2
## 0.02924#create a dataframe with the simulated data and the true data
df <- data.frame(x$sim_1, x$sim_2, x$sim_3, week_kuds$KUD95, week_kuds$LengthStd, week_kuds$Habitat, week_kuds$ComImport)
#plot KUD95 (real and simulated) against Length Std
grid.arrange(ggplot(data= df, aes(x = week_kuds.LengthStd, y=week_kuds.KUD95)) + geom_point(col="black") + scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("real KUD95"), ggplot(data= df, aes(x = week_kuds.LengthStd, y=x.sim_1)) + geom_point(col="lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("KUD95 simulation 1"), ggplot(data= df, aes(x = week_kuds.LengthStd, y=x.sim_2)) + geom_point(col="deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("KUD95 simulation 2"), ggplot(data= df, aes(x = week_kuds.LengthStd, y=x.sim_3)) + geom_point(col="deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Length Std") + ylab("KUD95 simulation 3"))
#plot KUD95 (real and simulated) against Commercial Importance
grid.arrange(ggplot(data = df, aes(x = week_kuds.ComImport, y=week_kuds.KUD95)) +
geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_1)) +
geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_2)) +
geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 3"), ncol = 4)
#plot KUD95 (real and simulated) against Habitat
grid.arrange(ggplot(data = df, aes(x = week_kuds.Habitat, y=week_kuds.KUD95)) +
geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_1)) +
geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_2)) +
geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 3"), ncol = 4)
The normality and homoscedasticity assumptions aren’t met. However, given the large dataset, this violation may not be a big problem. To investigate this, we simulated the response values and compared with the real ones. After observing the results, we concluded that the patterns were similar and the model can be said to be well adjusted.
Modelling KUD50
We tested the full linear model and checked the assumptions.
#Full model assumption checking
lmtotal1 <- lm(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2, data = week_kuds)
summary(lmtotal1)##
## Call:
## lm(formula = KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +
## Troph + Habitat + Migration + ComImport + ReceiverDensity +
## MonitArea_km2, data = week_kuds)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23144 -0.05035 -0.02695 0.03196 2.96212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.340e-02 1.129e-02 7.390 1.51e-13 ***
## LengthStd 7.539e-02 9.203e-03 8.192 2.68e-16 ***
## BodyMassStd -6.505e-02 7.941e-03 -8.191 2.71e-16 ***
## Longevity -1.222e-03 6.938e-05 -17.618 < 2e-16 ***
## Vulnerability 4.369e-04 1.255e-04 3.483 0.000497 ***
## Troph 1.245e-02 2.778e-03 4.483 7.39e-06 ***
## Habitatdemersal -1.728e-02 3.274e-03 -5.279 1.31e-07 ***
## Habitatpelagic-neritic 3.889e-02 3.619e-03 10.746 < 2e-16 ***
## Migrationoceanodromous 2.340e-02 3.235e-03 7.235 4.77e-13 ***
## ComImportmedium -3.367e-02 2.240e-03 -15.029 < 2e-16 ***
## ComImportminor 1.511e-03 8.762e-03 0.172 0.863122
## ReceiverDensity 6.079e-04 4.427e-05 13.733 < 2e-16 ***
## MonitArea_km2 9.996e-03 2.007e-04 49.813 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1185 on 25599 degrees of freedom
## Multiple R-squared: 0.1693, Adjusted R-squared: 0.1689
## F-statistic: 434.6 on 12 and 25599 DF, p-value: < 2.2e-16#Normality assumption
qqnorm(lmtotal1$residuals)
qqline(lmtotal1$residuals, col = 2)
#Homocedascity assumption
plot(lmtotal1$fitted.values, lmtotal1$residuals)
abline(h=0, col="red")
As expected, the linear model do not fit well to our data, since the assumptions are not met.
We tried to fit different types of models. We modeled the response against each predictor variable separately. We then compare the AIC of each model to see which one fitted better.
#Modeling (each variable separately)
#Length Standardised
lm_length1 <- lm(KUD50 ~ LengthStd, data=week_kuds)
glm_length1 <- glm(KUD50 ~ LengthStd, data=week_kuds, family=Gamma(link="log"))
gam_length1 <- gam(KUD50 ~ LengthStd, data=week_kuds, family=Gamma(link="log"))
glmmF_length1 <- glmmTMB(KUD50 ~ LengthStd + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_length1 <- glmmTMB(KUD50 ~ LengthStd + (1|Transmitter), data=week_kuds, family=Gamma(link="log")) #control = glmerControl(optimizer = "bobyqa")
glmmS_length1 <- glmmTMB(KUD50 ~ LengthStd + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_length1 <- gamm(KUD50 ~ s(LengthStd), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_length1 <- gamm4(KUD50 ~ s(LengthStd), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_length1 <- gamm(KUD50 ~ s(LengthStd), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4T_length1 <- gamm4(KUD50 ~ s(LengthStd), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_length1 <- gamm(KUD50 ~ s(LengthStd), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_length1 <- gamm4(KUD50 ~ s(LengthStd), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_length1, glm_length1, gam_length1, glmmF_length1, glmmT_length1, glmmS_length1)## df AIC
## lm_length1 3 -31828.99
## glm_length1 3 -52604.94
## gam_length1 3 -47252.02
## glmmF_length1 4 -65932.14
## glmmT_length1 4 -75923.01
## glmmS_length1 4 -61214.33summary(gammF_length1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 22567.53 22608.28 -11278.76
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.325791 1.325791 1.325791 1.325791 1.325791 1.325791 1.325791 1.325791
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.325026 0.3737981
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4855690 0.04762374 25563 -31.193875 0
## Xs(LengthStd)Fx1 -0.2212537 0.04684247 25563 -4.723356 0
## Correlation:
## X(Int)
## Xs(LengthStd)Fx1 -0.001
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7982212 -0.4745062 -0.2449103 0.1407538 23.7885183
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_length1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 10269.63 10310.39 -5129.817
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5
## StdDev: 0.0002300292 0.0002300292 0.0002300292 0.0002300292 0.0002300292
## Xr6 Xr7 Xr8
## StdDev: 0.0002300292 0.0002300292 0.0002300292
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3330887 0.2805653
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4839857 0.01196298 24761 -124.04815 0.0000
## Xs(LengthStd)Fx1 0.0170314 0.01153395 24761 1.47663 0.1398
## Correlation:
## X(Int)
## Xs(LengthStd)Fx1 0.082
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.56415741 -0.39930461 -0.11072128 0.08036937 16.90436482
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_length1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 29667.58 29708.33 -14828.79
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.677282 1.677282 1.677282 1.677282 1.677282 1.677282 1.677282 1.677282
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3382615 0.430117
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4517870 0.06289454 25581 -23.082877 0.0000
## Xs(LengthStd)Fx1 -0.1271003 0.05214480 25581 -2.437448 0.0148
## Correlation:
## X(Int)
## Xs(LengthStd)Fx1 0
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.5599808 -0.4844838 -0.2079017 0.1428929 22.5916755
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#BodyMass Standardised
lm_bodymass1 <- lm(KUD50 ~ BodyMassStd, data=week_kuds)
glm_bodymass1 <- glm(KUD50 ~ BodyMassStd, data=week_kuds, family=Gamma(link="log"))
gam_bodymass1 <- gam(KUD50 ~ BodyMassStd, data=week_kuds, family=Gamma(link="log"))
glmmF_bodymass1 <- glmmTMB(KUD50 ~ BodyMassStd + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_bodymass1 <- glmmTMB(KUD50 ~ BodyMassStd + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_bodymass1 <- glmmTMB(KUD50 ~ BodyMassStd + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_bodymass1 <- gamm(KUD50 ~ s(BodyMassStd), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_bodymass1 <- gamm4(KUD50 ~ s(BodyMassStd), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_bodymass1 <- gamm(KUD50 ~ s(BodyMassStd), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4T_bodymass1 <- gamm4(KUD50 ~ s(BodyMassStd), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_bodymass1 <- gamm(KUD50 ~ s(BodyMassStd), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_bodymass1 <- gamm4(KUD50 ~ s(BodyMassStd), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_bodymass1, glm_bodymass1, gam_bodymass1, glmmF_bodymass1, glmmT_bodymass1, glmmS_bodymass1)## df AIC
## lm_bodymass1 3 -31877.38
## glm_bodymass1 3 -52704.33
## gam_bodymass1 3 -47446.11
## glmmF_bodymass1 4 -65875.79
## glmmT_bodymass1 4 -75920.93
## glmmS_bodymass1 4 -61148.93summary(gammF_bodymass1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 22765.65 22806.4 -11377.82
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.675688 1.675688 1.675688 1.675688 1.675688 1.675688 1.675688 1.675688
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.3230223 0.3752228
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4907706 0.04734959 25563 -31.484343 0
## Xs(BodyMassStd)Fx1 -0.3614522 0.08382403 25563 -4.312036 0
## Correlation:
## X(Int)
## Xs(BodyMassStd)Fx1 0.005
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.8017544 -0.4697812 -0.2306646 0.1349153 24.2120975
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_bodymass1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 10267.54 10308.3 -5128.772
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.000134808 0.000134808 0.000134808 0.000134808 0.000134808 0.000134808
## Xr7 Xr8
## StdDev: 0.000134808 0.000134808
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3322417 0.2805759
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4863807 0.011918936 24761 -124.70750 0.000
## Xs(BodyMassStd)Fx1 0.0102556 0.008470971 24761 1.21067 0.226
## Correlation:
## X(Int)
## Xs(BodyMassStd)Fx1 -0.064
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.56391491 -0.39721582 -0.11012062 0.08031375 16.89270454
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_bodymass1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30072.04 30112.8 -15031.02
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.803092 1.803092 1.803092 1.803092 1.803092 1.803092 1.803092 1.803092
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3365053 0.4335209
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4561697 0.06262829 25581 -23.250989 0
## Xs(BodyMassStd)Fx1 -0.4317329 0.09563974 25581 -4.514158 0
## Correlation:
## X(Int)
## Xs(BodyMassStd)Fx1 0.007
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.5658935 -0.4997306 -0.2348428 0.1312459 21.6077413
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#Longevity
lm_longevity1 <- lm(KUD50 ~ Longevity, data=week_kuds)
glm_longevity1 <- glm(KUD50 ~ Longevity, data=week_kuds, family=Gamma(link="log"))
gam_longevity1 <- gam(KUD50 ~ Longevity, data=week_kuds, family=Gamma(link="log"))
glmmF_longevity1 <- glmmTMB(KUD50 ~ Longevity + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_longevity1 <- glmmTMB(KUD50 ~ Longevity + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_longevity1 <- glmmTMB(KUD50 ~ Longevity + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_longevity1 <- gamm(KUD50 ~ s(Longevity), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_longevity1 <- gamm4(KUD50 ~ s(Longevity), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_longevity1 <- gamm(KUD50 ~ s(Longevity), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4T_longevity1 <- gamm4(KUD50 ~ s(Longevity), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_longevity1 <- gamm(KUD50 ~ s(Longevity), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_longevity1 <- gamm4(KUD50 ~ s(Longevity), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_longevity1, glm_longevity1, gam_longevity1, glmmF_longevity1, glmmT_longevity1, glmmS_longevity1)## df AIC
## lm_longevity1 3 -32045.20
## glm_longevity1 3 -53090.12
## gam_longevity1 3 -47897.16
## glmmF_longevity1 4 -65829.55
## glmmT_longevity1 4 -75930.00
## glmmS_longevity1 4 -61149.69summary(gammF_longevity1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 23189.64 23230.4 -11589.82
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.004302762 0.004302762 0.004302762 0.004302762 0.004302762 0.004302762
## Xr7 Xr8
## StdDev: 0.004302762 0.004302762
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.3111037 0.3786781
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4941362 0.04667071 25564 -32.01443 0.0000
## Xs(Longevity)Fx1 0.0644094 0.05228794 46 1.23182 0.2243
## Correlation:
## X(Int)
## Xs(Longevity)Fx1 -0.21
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7704567 -0.4788453 -0.2238381 0.1224854 24.8902216
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_longevity1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 10223.12 10263.88 -5106.562
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.1358 1.1358 1.1358 1.1358 1.1358 1.1358 1.1358 1.1358
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3195612 0.2805209
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4925891 0.01268497 24761 -117.66595 0.0000
## Xs(Longevity)Fx1 -0.2048714 0.18981738 24761 -1.07931 0.2805
## Correlation:
## X(Int)
## Xs(Longevity)Fx1 -0.003
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.56380928 -0.40303735 -0.11176179 0.08279978 16.91852508
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_longevity1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30353.56 30394.31 -15171.78
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5
## StdDev: 0.0006308293 0.0006308293 0.0006308293 0.0006308293 0.0006308293
## Xr6 Xr7 Xr8
## StdDev: 0.0006308293 0.0006308293 0.0006308293
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3268438 0.4362716
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4700166 0.06584704 25582 -22.324718 0.0000
## Xs(Longevity)Fx1 0.0749604 0.08582418 28 0.873419 0.3899
## Correlation:
## X(Int)
## Xs(Longevity)Fx1 -0.383
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.5360005 -0.5037871 -0.2220684 0.1272389 22.7508061
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#Vulnerability
lm_vulnerability1 <- lm(KUD50 ~ Vulnerability, data=week_kuds)
glm_vulnerability1 <- glm(KUD50 ~ Vulnerability, data=week_kuds, family=Gamma(link="log"))
gam_vulnerability1 <- gam(KUD50 ~ Vulnerability, data=week_kuds, family=Gamma(link="log"))
glmmF_vulnerability1 <- glmmTMB(KUD50 ~ Vulnerability + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_vulnerability1 <- glmmTMB(KUD50 ~ Vulnerability + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_vulnerability1 <- glmmTMB(KUD50 ~ Vulnerability + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_vulnerability1 <- gamm(KUD50 ~ s(Vulnerability), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_vulnerability1 <- gamm4(KUD50 ~ s(Vulnerability), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_vulnerability1 <- gamm(KUD50 ~ s(Vulnerability), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4T_vulnerability1 <- gamm4(KUD50 ~ s(Vulnerability), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_vulnerability1 <- gamm(KUD50 ~ s(Vulnerability), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_vulnerability1 <- gamm4(KUD50 ~ s(Vulnerability), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_vulnerability1, glm_vulnerability1, gam_vulnerability1, glmmF_vulnerability1, glmmT_vulnerability1, glmmS_vulnerability1)## df AIC
## lm_vulnerability1 3 -32005.03
## glm_vulnerability1 3 -52955.22
## gam_vulnerability1 3 -47660.03
## glmmF_vulnerability1 4 -65829.82
## glmmT_vulnerability1 4 -75921.04
## glmmS_vulnerability1 4 -61151.52summary(gammF_vulnerability1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 23189.07 23229.83 -11589.54
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.001932727 0.001932727 0.001932727 0.001932727 0.001932727 0.001932727
## Xr7 Xr8
## StdDev: 0.001932727 0.001932727
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.3109426 0.3786742
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4568054 0.04938715 25564 -29.49766 0.0000
## Xs(Vulnerability)Fx1 0.0495008 0.03711036 46 1.33388 0.1888
## Correlation:
## X(Int)
## Xs(Vulnerability)Fx1 0.383
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7700272 -0.4789700 -0.2240155 0.1229565 24.8951430
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_vulnerability1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 10266.08 10306.83 -5128.039
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 7.45902e-05 7.45902e-05 7.45902e-05 7.45902e-05 7.45902e-05 7.45902e-05
## Xr7 Xr8
## StdDev: 7.45902e-05 7.45902e-05
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3314166 0.2805896
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4887249 0.01205047 24761 -123.54085 0.0000
## Xs(Vulnerability)Fx1 -0.0165179 0.01067642 24761 -1.54713 0.1218
## Correlation:
## X(Int)
## Xs(Vulnerability)Fx1 0.174
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.56458541 -0.39765169 -0.11013309 0.08013874 16.89163537
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_vulnerability1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30351.3 30392.06 -15170.65
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.006845411 0.006845411 0.006845411 0.006845411 0.006845411 0.006845411
## Xr7 Xr8
## StdDev: 0.006845411 0.006845411
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3185965 0.4362648
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.3922752 0.06867512 25582 -20.27336 0.000
## Xs(Vulnerability)Fx1 0.0700168 0.04340812 28 1.61299 0.118
## Correlation:
## X(Int)
## Xs(Vulnerability)Fx1 0.501
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.5353903 -0.5038038 -0.2214454 0.1284833 22.7568854
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#Troph
lm_troph1 <- lm(KUD50 ~ Troph, data=week_kuds)
glm_troph1 <- glm(KUD50 ~ Troph, data=week_kuds, family=Gamma(link="log"))
gam_troph1 <- gam(KUD50 ~ Troph, data=week_kuds, family=Gamma(link="log"))
glmmF_troph1 <- glmmTMB(KUD50 ~ Troph + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_troph1 <- glmmTMB(KUD50 ~ Troph + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_troph1 <- glmmTMB(KUD50 ~ Troph + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_troph1 <- gamm(KUD50 ~ s(Troph), random=list(File=~1), data= week_kuds, family=Gamma(link="log")) ##Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_troph1 <- gamm4(KUD50 ~ s(Troph), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_troph1 <- gamm(KUD50 ~ s(Troph), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4T_troph1 <- gamm4(KUD50 ~ s(Troph), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_troph1 <- gamm(KUD50 ~ s(Troph), random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_troph1 <- gamm4(KUD50 ~ s(Troph), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_troph1, glm_troph1, gam_troph1, glmmF_troph1, glmmT_troph1, glmmS_troph1)## df AIC
## lm_troph1 3 -31856.46
## glm_troph1 3 -52657.77
## gam_troph1 3 -47370.47
## glmmF_troph1 4 -65832.43
## glmmT_troph1 4 -75923.01
## glmmS_troph1 4 -61153.70summary(gammF_troph1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 23187.82 23228.57 -11588.91
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.007135181 0.007135181 0.007135181 0.007135181 0.007135181 0.007135181
## Xr7 Xr8
## StdDev: 0.007135181 0.007135181
##
## Formula: ~1 | File %in% g
## (Intercept) Residual
## StdDev: 0.3021429 0.3786846
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4575608 0.04580577 25564 -31.82046 0.0000
## Xs(Troph)Fx1 0.0942921 0.04422081 46 2.13230 0.0384
## Correlation:
## X(Int)
## Xs(Troph)Fx1 0.249
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7709195 -0.4790667 -0.2240647 0.1225451 24.8930121
##
## Number of Observations: 25612
## Number of Groups:
## g File %in% g
## 1 48summary(gammT_troph1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 10132.52 10173.28 -5061.262
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8
## StdDev: 1.453333 1.453333 1.453333 1.453333 1.453333 1.453333 1.453333 1.453333
##
## Formula: ~1 | Transmitter %in% g
## (Intercept) Residual
## StdDev: 0.3038602 0.2804331
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4836458 0.01234898 24761 -120.14318 0e+00
## Xs(Troph)Fx1 0.9243041 0.26110074 24761 3.54003 4e-04
## Correlation:
## X(Int)
## Xs(Troph)Fx1 -0.173
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.56446832 -0.40144680 -0.11106743 0.08418724 16.81202429
##
## Number of Observations: 25612
## Number of Groups:
## g Transmitter %in% g
## 1 850summary(gammS_troph1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30349.77 30390.52 -15169.88
##
## Random effects:
## Formula: ~Xr - 1 | g
## Structure: pdIdnot
## Xr1 Xr2 Xr3 Xr4 Xr5 Xr6
## StdDev: 0.008900372 0.008900372 0.008900372 0.008900372 0.008900372 0.008900372
## Xr7 Xr8
## StdDev: 0.008900372 0.008900372
##
## Formula: ~1 | Species %in% g
## (Intercept) Residual
## StdDev: 0.3060275 0.4362715
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4167364 0.05871496 25582 -24.129054 0.0000
## Xs(Troph)Fx1 0.1183704 0.05262428 28 2.249349 0.0325
## Correlation:
## X(Int)
## Xs(Troph)Fx1 0.234
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.5365063 -0.5036437 -0.2214987 0.1283573 22.7547814
##
## Number of Observations: 25612
## Number of Groups:
## g Species %in% g
## 1 30#Habitat
lm_habitat1 <- lm(KUD50 ~ Habitat, data=week_kuds)
glm_habitat1 <- glm(KUD50 ~ Habitat, data=week_kuds, family=Gamma(link="log"))
gam_habitat1 <- gam(KUD50 ~ Habitat, data=week_kuds, family=Gamma(link="log"))
glmmF_habitat1 <- glmmTMB(KUD50 ~ Habitat + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_habitat1 <- glmmTMB(KUD50 ~ Habitat + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_habitat1 <- glmmTMB(KUD50 ~ Habitat + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_habitat1 <- gamm(KUD50 ~ Habitat, random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_habitat1 <- gamm4(KUD50 ~ Habitat, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_habitat1 <- gamm(KUD50 ~ Habitat, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4T_habitat1 <- gamm4(KUD50 ~ Habitat, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_habitat1 <- gamm(KUD50 ~ Habitat, random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_habitat1 <- gamm4(KUD50 ~ Habitat, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_habitat1, glm_habitat1, gam_habitat1, glmmF_habitat1, glmmT_habitat1, glmmS_habitat1)## df AIC
## lm_habitat1 4 -32396.89
## glm_habitat1 4 -53780.41
## gam_habitat1 4 -48992.27
## glmmF_habitat1 5 -65850.73
## glmmT_habitat1 5 -75984.63
## glmmS_habitat1 5 -61164.81summary(gammF_habitat1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 23167.91 23208.66 -11578.95
##
## Random effects:
## Formula: ~1 | File
## (Intercept) Residual
## StdDev: 0.2443296 0.3786823
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4259941 0.06401175 25564 -22.277067 0.0000
## XHabitatdemersal -0.1985912 0.08033184 45 -2.472135 0.0173
## XHabitatpelagic-neritic 0.4191038 0.11928896 45 3.513350 0.0010
## Correlation:
## X(Int) XHbttd
## XHabitatdemersal -0.797
## XHabitatpelagic-neritic -0.537 0.428
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7722325 -0.4793527 -0.2234567 0.1233451 24.9069722
##
## Number of Observations: 25612
## Number of Groups: 48summary(gammT_habitat1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 10179.97 10220.73 -5084.987
##
## Random effects:
## Formula: ~1 | Transmitter
## (Intercept) Residual
## StdDev: 0.3192479 0.280432
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4521426 0.01666122 24761 -87.15706 0
## XHabitatdemersal -0.1138321 0.02350036 24761 -4.84384 0
## XHabitatpelagic-neritic 0.2326868 0.04417678 848 5.26718 0
## Correlation:
## X(Int) XHbttd
## XHabitatdemersal -0.697
## XHabitatpelagic-neritic -0.377 0.263
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.55905205 -0.39695996 -0.10939032 0.08198035 17.05296469
##
## Number of Observations: 25612
## Number of Groups: 850summary(gammS_habitat1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30333.2 30373.95 -15161.6
##
## Random effects:
## Formula: ~1 | Species
## (Intercept) Residual
## StdDev: 0.2456872 0.4362372
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4555964 0.08308074 25582 -17.520263 0.0000
## XHabitatdemersal -0.1437323 0.10493251 27 -1.369759 0.1820
## XHabitatpelagic-neritic 0.4808568 0.13893988 27 3.460899 0.0018
## Correlation:
## X(Int) XHbttd
## XHabitatdemersal -0.792
## XHabitatpelagic-neritic -0.598 0.473
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.5378088 -0.5036260 -0.2217697 0.1280249 22.7700811
##
## Number of Observations: 25612
## Number of Groups: 30#Migration
lm_migration1 <- lm(KUD50 ~ Migration, data=week_kuds)
glm_migration1 <- glm(KUD50 ~ Migration, data=week_kuds, family=Gamma(link="log"))
gam_migration1 <- gam(KUD50 ~ Migration, data=week_kuds, family=Gamma(link="log"))
glmmF_migration1 <- glmmTMB(KUD50 ~ Migration + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_migration1 <- glmmTMB(KUD50 ~ Migration + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_migration1 <- glmmTMB(KUD50 ~ Migration + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_migration1 <- gamm(KUD50 ~ Migration, random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_migration1 <- gamm4(KUD50 ~ Migration, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_migration1 <- gamm(KUD50 ~ Migration, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4T_migration1 <- gamm4(KUD50 ~ Migration, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_migration1 <- gamm(KUD50 ~ Migration, random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_migration1 <- gamm4(KUD50 ~ Migration, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_migration1, glm_migration1, gam_migration1, glmmF_migration1, glmmT_migration1, glmmS_migration1)## df AIC
## lm_migration1 3 -32191.53
## glm_migration1 3 -53333.40
## gam_migration1 3 -48532.86
## glmmF_migration1 4 -65839.26
## glmmT_migration1 4 -75937.60
## glmmS_migration1 4 -61157.96summary(gammF_migration1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 23180.12 23212.73 -11586.06
##
## Random effects:
## Formula: ~1 | File
## (Intercept) Residual
## StdDev: 0.2811582 0.3786919
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.5773729 0.04935664 25564 -31.95868 0e+00
## XMigrationoceanodromous 0.3200494 0.09041543 46 3.53977 9e-04
## Correlation:
## X(Int)
## XMigrationoceanodromous -0.546
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7707688 -0.4797200 -0.2235529 0.1234385 24.8976334
##
## Number of Observations: 25612
## Number of Groups: 48summary(gammT_migration1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 10241.27 10273.87 -5116.634
##
## Random effects:
## Formula: ~1 | Transmitter
## (Intercept) Residual
## StdDev: 0.3279284 0.2805539
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.5312416 0.01572497 24762 -97.37647 0
## XMigrationoceanodromous 0.1035623 0.02366378 848 4.37640 0
## Correlation:
## X(Int)
## XMigrationoceanodromous -0.665
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.56127361 -0.39901226 -0.10991915 0.08104294 16.92022478
##
## Number of Observations: 25612
## Number of Groups: 850summary(gammS_migration1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30339.13 30371.73 -15165.56
##
## Random effects:
## Formula: ~1 | Species
## (Intercept) Residual
## StdDev: 0.2844533 0.4362337
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.562664 0.06401759 25582 -24.409920 0.0000
## XMigrationoceanodromous 0.370545 0.11494195 28 3.223757 0.0032
## Correlation:
## X(Int)
## XMigrationoceanodromous -0.557
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.5365139 -0.5036895 -0.2220609 0.1279743 22.7623218
##
## Number of Observations: 25612
## Number of Groups: 30#ComImport
lm_comimport1 <- lm(KUD50 ~ ComImport, data=week_kuds)
glm_comimport1 <- glm(KUD50 ~ ComImport, data=week_kuds, family=Gamma(link="log"))
gam_comimport1 <- gam(KUD50 ~ ComImport, data=week_kuds, family=Gamma(link="log"))
glmmF_comimport1 <- glmmTMB(KUD50 ~ ComImport + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_comimport1 <- glmmTMB(KUD50 ~ ComImport + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_comimport1 <- glmmTMB(KUD50 ~ ComImport + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_comimport1 <- gamm(KUD50 ~ ComImport, random=list(File=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4F_comimport1 <- gamm4(KUD50 ~ ComImport, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_comimport1 <- gamm(KUD50 ~ ComImport, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4T_comimport1 <- gamm4(KUD50 ~ ComImport, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_comimport1 <- gamm(KUD50 ~ ComImport, random=list(Species=~1), data= week_kuds, family=Gamma(link="log")) #Preference for gmcv package since gamm4 uses glm methods##
## Maximum number of PQL iterations: 20## iteration 1## iteration 2## iteration 3## iteration 4#gamm4S_comimport1 <- gamm4(KUD50 ~ ComImport, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))
AIC(lm_comimport1, glm_comimport1, gam_comimport1, glmmF_comimport1, glmmT_comimport1, glmmS_comimport1)## df AIC
## lm_comimport1 4 -31846.21
## glm_comimport1 4 -52643.88
## gam_comimport1 4 -47290.79
## glmmF_comimport1 5 -65827.93
## glmmT_comimport1 5 -75942.37
## glmmS_comimport1 5 -61148.70summary(gammF_comimport1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 23189.53 23230.28 -11589.76
##
## Random effects:
## Formula: ~1 | File
## (Intercept) Residual
## StdDev: 0.3102903 0.3786791
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4994421 0.06356000 25564 -23.590971 0.0000
## XComImportmedium 0.0887424 0.09945657 45 0.892273 0.3770
## XComImportminor -0.1055033 0.13997297 45 -0.753741 0.4549
## Correlation:
## X(Int) XCmImprtmd
## XComImportmedium -0.639
## XComImportminor -0.454 0.290
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.7698920 -0.4788326 -0.2243400 0.1234447 24.8923718
##
## Number of Observations: 25612
## Number of Groups: 48summary(gammT_comimport1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 10261.3 10302.06 -5125.651
##
## Random effects:
## Formula: ~1 | Transmitter
## (Intercept) Residual
## StdDev: 0.3310625 0.280572
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4742920 0.01361534 24760 -108.28165 0.0000
## XComImportmedium -0.0174252 0.02764499 24760 -0.63032 0.5285
## XComImportminor -0.1969604 0.04180688 24760 -4.71120 0.0000
## Correlation:
## X(Int) XCmImprtmd
## XComImportmedium -0.479
## XComImportminor -0.144 0.069
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.56384816 -0.39986609 -0.11021798 0.08085676 16.89063683
##
## Number of Observations: 25612
## Number of Groups: 850summary(gammS_comimport1$lme)## Linear mixed-effects model fit by maximum likelihood
## Data: data
## AIC BIC logLik
## 30350.69 30391.44 -15170.34
##
## Random effects:
## Formula: ~1 | Species
## (Intercept) Residual
## StdDev: 0.3218846 0.4362546
##
## Variance function:
## Structure: fixed weights
## Formula: ~invwt
## Fixed effects: list(fixed)
## Value Std.Error DF t-value p-value
## X(Intercept) -1.4083847 0.1019478 25582 -13.814759 0.0000
## XComImportmedium 0.0036375 0.1359229 27 0.026762 0.9788
## XComImportminor -0.1964511 0.1658027 27 -1.184848 0.2464
## Correlation:
## X(Int) XCmImprtmd
## XComImportmedium -0.750
## XComImportminor -0.615 0.461
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.5357896 -0.5038047 -0.2220157 0.1279555 22.7519889
##
## Number of Observations: 25612
## Number of Groups: 30Observing the AIC values of all models adjusted, we can see that the one that better fits the data is the GLMM, with the Transmitter as random effect. Looking for all the GLMM adjusted, with higher attention to the variables used as random effects, we see that the models adjusted with Transmitter are better than the ones adjusted with File, and lastly Species.
To analyse and support the importance of each variable assigned to the random effect, we also perform a random forest model, including all three variables: Transmitter, File and Species.
random_three <- rpart(KUD50 ~ Species + Transmitter + File, data = week_kuds)
summary(random_three)## Call:
## rpart(formula = KUD50 ~ Species + Transmitter + File, data = week_kuds)
## n= 25612
##
## CP nsplit rel error xerror xstd
## 1 0.33008309 0 1.0000000 1.0001259 0.04804281
## 2 0.05974581 1 0.6699169 0.6936541 0.03896310
## 3 0.04250898 2 0.6101711 0.6555445 0.03716306
## 4 0.01662605 3 0.5676621 0.6169190 0.03695824
## 5 0.01000000 4 0.5510361 0.6155962 0.03656244
##
## Variable importance
## Transmitter File Species
## 69 21 10
##
## Node number 1: 25612 observations, complexity param=0.3300831
## mean=0.2267318, MSE=0.01689573
## left son=2 (22066 obs) right son=3 (3546 obs)
## Primary splits:
## Transmitter splits as LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLRLLLLLLRLLLLLRLLLLLLLLLLRLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLRLLLLRLLLRRLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLRLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRRRRRRRRLRRRRLRLRLRLRRRRLLRLRLLRRRRRRRRLLRRRRRRRRLLRLRLLLRLLRRLLLRLRRLRRRRRRLRLLLRLRLLLLLRRRLLRRLLLLLLLLLLLLRLLLLLLLRRLLLRLRLLRLLLLLLLLRRRLLLLLLLLLLLLRRLLLLRRLLRRRLLLLLLRLLLLLLRLLRLLLLRRLLLRRRLRLLLLLLRRLLLLLLLLLLLLLLLLLLRRLRLLLLRRLLRLRRRLLLRLLLRRLLRRRLLRLRRLLRRLRRRLLRLLRLLRLLLLRRRRLRRLLLLLLLLLLLLLLLLLLLLRLLLLLLRLLLLLLLLLRLLLRLLRLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLRLLLRLLLLLLLRLLLLLLLLLLLLLLLLLLLRRL, improve=0.3300831, (0 missing)
## File splits as LLRLRRLLLLLLLLLLLLLLLLRLLLLRLLLLLLRLLLLLLLRLRLLL, improve=0.2033131, (0 missing)
## Species splits as RRRLRLLLRLLLLLRLRLLLLRLLLLLRLL, improve=0.1134862, (0 missing)
## Surrogate splits:
## File splits as LLLLRLLLLLLLLLLLLLLLLLRLLLLRLLLLLLRLLLLLLLLLRLLL, agree=0.912, adj=0.362, (0 split)
## Species splits as LLLLLLLLRLLLLLRLLLLLLRLLLLLLLL, agree=0.881, adj=0.139, (0 split)
##
## Node number 2: 22066 observations, complexity param=0.04250898
## mean=0.1967949, MSE=0.003629296
## left son=4 (17166 obs) right son=5 (4900 obs)
## Primary splits:
## Transmitter splits as LRLLRLLLLLLLLLLLLLLLLLRRRLLLRRLRRRRRLRR-LLRLLLRLLRRLLLLRRLLRRRLLRRLLLLRRLRRLRRRRRR-LLLLLRLLLRLLLLRLR-RLLLLLLLRLLRLRLRRLLLLLLRLLLRLLLLLLRRLLLLLLLLLRLRLLRLLRL-RLRLRLLLL-LRRRLR-RRRRR-LRRLLRRLRL-RRL-RLRLLLLLLLLLLLLLLLLLLRLLLLLLL-LLLLLLLLLLLRRLLLLLLLLLLLLLLLLLRRLLLLLLRLLRRLLRLL-L-LLLL-LRR--RR-LLLRLLLLLRLLLRLLLLLLLLLLLLLLLLRLRLLRLLRLRL-RL-R-LLLRLRRRLLRLLLLRLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLL-LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL--------R----L-R-R-L----LR-L-LL--------RR--------LL-L-RLL-LL--LLR-R--L------L-RLR-L-LRRRL---LR--RRLLLLLLLLLR-RRLLLLL--LLL-R-RL-LLRRLLLR---LLLLLLLLLLLL--LLRR--LL---RLLLLR-RRLLLL-RR-RRLL--LLR---R-LLRRLL--LLLLRRLLLLLLRRRRRR--L-LLRL--LL-R---RLR-LRL--RR---LL-R--LL--R---RR-LL-LL-LLLL----L--LLRLRLLLRRRRRLRLLRLR-RRLLLR-LRRLRLLRL-RLR-RR-RR-RRRRRLLRLRLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRRLLLLLLLLLLLLLLRLLL-LL-LLR-LRLLLLR-LRRLRRRLRLLRLLLLLLR--L, improve=0.22969690, (0 missing)
## File splits as RRRRLRRLLRRLLRLLLLRLLL-RLLLRLLRLLR-LLRLLLLRLRRLL, improve=0.06237878, (0 missing)
## Species splits as RRRRRLLR-LRLLLRLRRRLR-LLLLLLLL, improve=0.04785787, (0 missing)
## Surrogate splits:
## File splits as RRRLLRLLLLLLLLLLLLRLLL-RLLLRLLRLLL-LLLLLLLRLRLLL, agree=0.813, adj=0.160, (0 split)
## Species splits as RRLLRLLL-LRLLLRLLLLLL-LLLLLLLL, agree=0.803, adj=0.114, (0 split)
##
## Node number 3: 3546 observations, complexity param=0.05974581
## mean=0.4130231, MSE=0.05916846
## left son=6 (2630 obs) right son=7 (916 obs)
## Primary splits:
## Transmitter splits as ---------------------------------------L------------------------------------------L-----------------L-------------------------------------------------------L---------L------L-----L----------L---L-----------------------------L------------------------------------------------R-L----L---LL--L------------------------------------------L--R-L--------------------------------------------------L------------------------------RRRRLLRR-LLLL-R-R-L-RLRL--R-R--LRRRRRRL--LLRRLRLL--L-R---L--LR---L-LR-LRRLRL-L---R-L-----LLL--LL------------R-------LR---L-L--L--------LLL------------LL----LL--LLL------L------L--L----LL---LLL-L------LL------------------RL-L----LR--L-LRL---L---RL--LLL--R-LL--LR-RRL--L--L--R----LLRL-LL--------------------L------L---------R---L--L--L-------------------------------------------------------------L--L---L-------L-------------------LL-, improve=0.12322500, (0 missing)
## File splits as -LLLRRLL-LLL-----RLRL-R-L-RRL-----R---LL--R-RL--, improve=0.05036611, (0 missing)
## Species splits as RLRL--RLR--LLRR-R-L--R--LL-R--, improve=0.04419281, (0 missing)
## Surrogate splits:
## Species splits as LLLL--RLR--LLRR-L-L--R--LL-L--, agree=0.789, adj=0.184, (0 split)
## File splits as -LLLLLLL-LLL-----RLLL-R-L-RRL-----R---LL--L-LL--, agree=0.789, adj=0.184, (0 split)
##
## Node number 4: 17166 observations
## mean=0.1813689, MSE=0.00163798
##
## Node number 5: 4900 observations
## mean=0.2508361, MSE=0.006851313
##
## Node number 6: 2630 observations
## mean=0.3626308, MSE=0.03018033
##
## Node number 7: 916 observations, complexity param=0.01662605
## mean=0.5577085, MSE=0.1141737
## left son=14 (715 obs) right son=15 (201 obs)
## Primary splits:
## Transmitter splits as ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------L------------------------------------------------------------L-----------------------------------------------------------------------------------LRRR--LL------R-R---R-L---R-L---RLRRLL-----LL-R------R-------R------L--LL-L------R--------------------------L--------R------------------------------------------------------------------------------------------------------L--------L-----L--------R--------L------R-RL---------L------R-----------------------------------------L-------------------------------------------------------------------------------------------------------------, improve=0.06879362, (0 missing)
## Species splits as -LR---LLR--L-RL-R----L-----L--, improve=0.04110001, (0 missing)
## File splits as --L-R------------LL---R---RLL-----L-------R-L---, improve=0.04110001, (0 missing)
## Surrogate splits:
## Species splits as -LL---LLR--L-RL-R----L-----L--, agree=0.823, adj=0.194, (0 split)
## File splits as --L-L------------LL---R---RLL-----L-------R-L---, agree=0.823, adj=0.194, (0 split)
##
## Node number 14: 715 observations
## mean=0.5107189, MSE=0.08078683
##
## Node number 15: 201 observations
## mean=0.7248607, MSE=0.1971435To choose which one of these variables include in the model, we tested all possible combinations.
glmmT_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmF_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmS_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Species), data=week_kuds, family=Gamma(link="log"))
glmmTF_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmTS_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log"))
glmmFS_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))
glmmTFS_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))
AIC(glmmT_total1, glmmF_total1, glmmS_total1, glmmTF_total1, glmmTS_total1, glmmFS_total1, glmmTFS_total1)## df AIC
## glmmT_total1 15 -76204.64
## glmmF_total1 15 -65971.34
## glmmS_total1 15 -63412.70
## glmmTF_total1 16 -76365.69
## glmmTS_total1 16 -76245.35
## glmmFS_total1 16 -65969.34
## glmmTFS_total1 17 -76363.69#Now we need to compare if the models are statistically different or not
anova(glmmT_total1, glmmTF_total1) #significant differences, this two models differ statistically## Data: week_kuds
## Models:
## glmmT_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total1: MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTF_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total1: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmT_total1 15 -76205 -76082 38117 -76235
## glmmTF_total1 16 -76366 -76235 38199 -76398 163.05 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(glmmT_total1, glmmTFS_total1) #significant differences, this two models differ statistically## Data: week_kuds
## Models:
## glmmT_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total1: MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTFS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total1: MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmT_total1 15 -76205 -76082 38117 -76235
## glmmTFS_total1 17 -76364 -76225 38199 -76398 163.05 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(glmmTF_total1, glmmTFS_total1) #non significant differences, this two models do not differ statistically## Data: week_kuds
## Models:
## glmmTF_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total1: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTFS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total1: MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total1 16 -76366 -76235 38199 -76398
## glmmTFS_total1 17 -76364 -76225 38199 -76398 0 1 1anova(glmmTF_total1, glmmTS_total1) #non significant differences, this two models do not differ statistically## Data: week_kuds
## Models:
## glmmTF_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total1: MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total1: MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total1 16 -76366 -76235 38199 -76398
## glmmTS_total1 16 -76245 -76115 38139 -76277 0 0 1anova(glmmTS_total1, glmmTFS_total1) #significant differences, this two models differ statistically## Data: week_kuds
## Models:
## glmmTS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total1: MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## glmmTFS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total1: Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total1: MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTS_total1 16 -76245 -76115 38139 -76277
## glmmTFS_total1 17 -76364 -76225 38199 -76398 120.33 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#choose the simplest model with the lower AIC, that is statistically different from the others, which means the model with Transmitter and File as random effect (glmmTF_total1)Having chosen the best variable to include in the model as random effect, we now must do a backward stepwise selection, to investigate which are the best predictor variables to explain the response behaviour.
Final1.1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final1.1)## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +
## Troph + Habitat + Migration + ComImport + ReceiverDensity +
## MonitArea_km2 + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76365.7 -76235.3 38198.8 -76397.7 25596
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05909 0.2431
## File (Intercept) 0.02406 0.1551
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6757908 0.3487319 -4.805 1.54e-06 ***
## LengthStd 0.3226108 0.1416795 2.277 0.0228 *
## BodyMassStd -0.0200972 0.1064550 -0.189 0.8503
## Longevity -0.0031883 0.0024332 -1.310 0.1901
## Vulnerability -0.0022852 0.0034940 -0.654 0.5131
## Troph 0.0344231 0.0942440 0.365 0.7149
## Habitatdemersal -0.1499151 0.0915117 -1.638 0.1014
## Habitatpelagic-neritic 0.2870154 0.1332767 2.154 0.0313 *
## Migrationoceanodromous 0.0605097 0.1034365 0.585 0.5586
## ComImportmedium -0.1516597 0.0632606 -2.397 0.0165 *
## ComImportminor -0.1719519 0.1078182 -1.595 0.1107
## ReceiverDensity 0.0004611 0.0008730 0.528 0.5974
## MonitArea_km2 0.0250441 0.0054637 4.584 4.57e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final2.1 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final2.1)## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat +
## Migration + ComImport + ReceiverDensity + MonitArea_km2 +
## (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76367.7 -76245.4 38198.8 -76397.7 25597
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05909 0.2431
## File (Intercept) 0.02417 0.1555
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6777177 0.3491815 -4.805 1.55e-06 ***
## LengthStd 0.3029426 0.0959621 3.157 0.00159 **
## Longevity -0.0032084 0.0024350 -1.318 0.18763
## Vulnerability -0.0022043 0.0034736 -0.635 0.52570
## Troph 0.0345943 0.0944046 0.366 0.71403
## Habitatdemersal -0.1466255 0.0899547 -1.630 0.10310
## Habitatpelagic-neritic 0.2883959 0.1333152 2.163 0.03052 *
## Migrationoceanodromous 0.0612599 0.1035349 0.592 0.55406
## ComImportmedium -0.1533624 0.0627234 -2.445 0.01448 *
## ComImportminor -0.1748997 0.1068404 -1.637 0.10163
## ReceiverDensity 0.0004786 0.0008695 0.550 0.58204
## MonitArea_km2 0.0251816 0.0054229 4.644 3.42e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final3.1 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Vulnerability + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final3.1)## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Longevity + Vulnerability + Habitat + Migration +
## ComImport + ReceiverDensity + MonitArea_km2 + (1 | Transmitter) +
## (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76369.5 -76255.4 38198.8 -76397.5 25598
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05906 0.2430
## File (Intercept) 0.02431 0.1559
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5741547 0.2045551 -7.696 1.41e-14 ***
## LengthStd 0.3001179 0.0956839 3.137 0.00171 **
## Longevity -0.0029064 0.0022961 -1.266 0.20559
## Vulnerability -0.0017496 0.0032547 -0.538 0.59089
## Habitatdemersal -0.1584008 0.0843081 -1.879 0.06027 .
## Habitatpelagic-neritic 0.3161475 0.1102086 2.869 0.00412 **
## Migrationoceanodromous 0.0488521 0.0981469 0.498 0.61866
## ComImportmedium -0.1518711 0.0627285 -2.421 0.01547 *
## ComImportminor -0.1665704 0.1046137 -1.592 0.11133
## ReceiverDensity 0.0005643 0.0008395 0.672 0.50148
## MonitArea_km2 0.0252431 0.0054316 4.647 3.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final4.1 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Vulnerability + Habitat + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final4.1)## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Longevity + Vulnerability + Habitat + ComImport +
## ReceiverDensity + MonitArea_km2 + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76371.3 -76265.3 38198.6 -76397.3 25599
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05906 0.2430
## File (Intercept) 0.02459 0.1568
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5654335 0.2046327 -7.650 2.01e-14 ***
## LengthStd 0.2962118 0.0954697 3.103 0.00192 **
## Longevity -0.0029135 0.0023064 -1.263 0.20651
## Vulnerability -0.0013971 0.0031927 -0.438 0.66167
## Habitatdemersal -0.1850843 0.0655367 -2.824 0.00474 **
## Habitatpelagic-neritic 0.3362873 0.1031270 3.261 0.00111 **
## ComImportmedium -0.1511792 0.0630009 -2.400 0.01641 *
## ComImportminor -0.1626389 0.1047580 -1.553 0.12054
## ReceiverDensity 0.0006034 0.0008396 0.719 0.47230
## MonitArea_km2 0.0250351 0.0054389 4.603 4.16e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final5.1 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Habitat + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final5.1)## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Longevity + Habitat + ComImport + ReceiverDensity +
## MonitArea_km2 + (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76373.1 -76275.3 38198.5 -76397.1 25600
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05901 0.2429
## File (Intercept) 0.02489 0.1578
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6451203 0.0942083 -17.463 < 2e-16 ***
## LengthStd 0.2924062 0.0951627 3.073 0.00212 **
## Longevity -0.0034646 0.0019400 -1.786 0.07412 .
## Habitatdemersal -0.1791802 0.0643928 -2.783 0.00539 **
## Habitatpelagic-neritic 0.3224631 0.0985137 3.273 0.00106 **
## ComImportmedium -0.1483151 0.0629554 -2.356 0.01848 *
## ComImportminor -0.1393345 0.0905728 -1.538 0.12396
## ReceiverDensity 0.0006217 0.0008426 0.738 0.46059
## MonitArea_km2 0.0257238 0.0052279 4.920 8.63e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final6.1 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final6.1)## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Longevity + Habitat + ComImport + MonitArea_km2 +
## (1 | Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76374.5 -76284.9 38198.3 -76396.5 25601
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05902 0.2429
## File (Intercept) 0.02524 0.1589
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.621117 0.088699 -18.277 < 2e-16 ***
## LengthStd 0.293851 0.095198 3.087 0.00202 **
## Longevity -0.003370 0.001947 -1.731 0.08346 .
## Habitatdemersal -0.181837 0.064641 -2.813 0.00491 **
## Habitatpelagic-neritic 0.310891 0.097686 3.183 0.00146 **
## ComImportmedium -0.141153 0.062545 -2.257 0.02402 *
## ComImportminor -0.131797 0.090437 -1.457 0.14502
## MonitArea_km2 0.023727 0.004506 5.265 1.4e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final7.1 <- glmmTMB(KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|File), data = week_kuds, family = Gamma(link="log"))
summary(Final7.1)## Family: Gamma ( log )
## Formula:
## KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 |
## Transmitter) + (1 | File)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76373.7 -76292.2 38196.8 -76393.7 25602
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05900 0.2429
## File (Intercept) 0.02763 0.1662
## Number of obs: 25612, groups: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.713924 0.073433 -23.340 < 2e-16 ***
## LengthStd 0.309484 0.095013 3.257 0.001125 **
## Habitatdemersal -0.177808 0.066763 -2.663 0.007739 **
## Habitatpelagic-neritic 0.354832 0.098030 3.620 0.000295 ***
## ComImportmedium -0.137665 0.064810 -2.124 0.033660 *
## ComImportminor -0.097442 0.091080 -1.070 0.284685
## MonitArea_km2 0.023431 0.004661 5.027 4.97e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(Final6.1, Final7.1) #as there is no evidence that this two models are statistically different from each other, we must choose the simplest one, the one with less variables (Final7)## Data: week_kuds
## Models:
## Final7.1: KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7.1: Transmitter) + (1 | File), zi=~0, disp=~1
## Final6.1: KUD50 ~ LengthStd + Longevity + Habitat + ComImport + MonitArea_km2 + , zi=~0, disp=~1
## Final6.1: (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## Final7.1 10 -76374 -76292 38197 -76394
## Final6.1 11 -76375 -76285 38198 -76397 2.8744 1 0.09 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ranef(Final7.1) #deviation from intercept for each Transmitter and File group## $Transmitter
## (Intercept)
## 1 0.0437489110
## 10 0.2664459378
## 11 -0.0022539848
## 12 -0.2289394994
## 123 0.2229732378
## 1248320 -0.0330426583
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## 1248322 -0.0323196007
## 1248323 -0.0083820762
## 1248324 -0.0469725047
## 1248325 -0.0036757022
## 1248326 0.0046691444
## 1248327 -0.0264712348
## 1248328 -0.0035708651
## 1248329 -0.0113237208
## 1248330 0.0242038322
## 1248331 0.0130826759
## 1248332 0.0149711968
## 1248333 0.0408104429
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## 1512 -0.2387594192
## 1513 -0.0994555905
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## 1517 0.1551576113
## 1519 -0.1192387545
## 1521 0.1061108072
## 1522 0.2161868039
## 1523 0.0494485329
## 1524 0.0196634318
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## Linguado 01 0.3400362335
## Linguado 02 -0.1872437754
## Linguado 03 -0.2328909713
## Linguado 04 0.2504888411
## Linguado 05 -0.0185204747
## Linguado 06 -0.1535129338
## Linguado 07 0.1568234420
## Linguado 08 0.2970364386
## Linguado 09 -0.0704111794
## Linguado 10 0.0531625089
## Linguado 11 -0.1470486914
## Linguado 12 -0.1150117729
## Linguado 13 -0.2497609417
## Linguado 14 -0.2623030683
## Linguado 15 0.1984403627
## Linguado 16 0.2738227595
## Linguado 17 -0.1525139663
## Linguado 18 0.0455435617
## Linguado 19 0.0141613276
## Linguado 20 -0.0643875229
## Linguado 21 0.0424445429
## Linguado 22 0.2527856841
## Sargo 02 0.2787751474
## Sargo 03 -0.1636177233
## Sargo 05 0.0163015758
## Sargo 06 -0.0236514327
## Sargo 07 -0.2267725083
## Sargo 08 0.2764597135
## Sargo 09 -0.1646329012
## Sargo 12 -0.1661251459
## Sargo 13 -0.1255827254
## Sargo 14 -0.1517012810
## Sargo 15 -0.1190068946
## Sargo 16 -0.1180598723
## Sargo 17 0.0297242833
## Sargo 18 0.4828842019
## Sargo 19 0.5272823112
## Sargo 20 -0.1723714157
##
## $File
## (Intercept)
## Dactylopterus_volitans -0.0496245332
## Dentex_dentex1 0.1462001388
## Dentex_dentex2 -0.0830177854
## Dicentrarchus_labrax1 -0.1959428843
## Dicentrarchus_labrax2 0.2610650422
## Diplodus_cervinus 0.0350246169
## Diplodus_sargus1 0.0842340270
## Diplodus_sargus2 0.1329092809
## Diplodus_sargus3 -0.0146918817
## Diplodus_sargus4 0.0248203784
## Diplodus_sargus5 0.0816437426
## Diplodus_sargus6 -0.2241798966
## Diplodus_vulgaris1 0.2344197018
## Diplodus_vulgaris2 0.0044716561
## Epinephelus_marginatus1 -0.1651389410
## Epinephelus_marginatus2 -0.0629877641
## Epinephelus_marginatus3 -0.0408069278
## Epinephelus_marginatus4 -0.2393455502
## Gadus_morhua1 0.1071258538
## Gadus_morhua2 -0.1477099044
## Gadus_morhua3 -0.0147658353
## Labrus_bergylta 0.1130078886
## Lichia_amia 0.2098598776
## Lithognathus_mormyrus -0.0453903091
## Pagellus_erythrinus 0.1263621499
## Pagrus_pagrus1 -0.2162318773
## Pagrus_pagrus2 0.1349086353
## Pomatomus_saltatrix 0.1434904344
## Pseudocaranx_dentex 0.0386085891
## Sciaena_umbra1 -0.0183953491
## Sciaena_umbra2 0.0004449315
## Scorpaena_porcus 0.0558652308
## Scorpaena_scrofa1 0.0600232561
## Scorpaena_scrofa2 -0.1937238395
## Seriola_dumerili 0.1901781517
## Seriola_rivoliana -0.2583167762
## Serranus_atricauda -0.1556735643
## Serranus_cabrilla 0.1653288823
## Serranus_scriba -0.0973691229
## Solea_senegalensis 0.1269514795
## Sparisoma_cretense -0.1721149414
## Sparus_aurata1 -0.0181567712
## Sparus_aurata2 0.2966743472
## Sphyraena_viridensis1 -0.3163050361
## Sphyraena_viridensis2 0.0256349581
## Spondyliosoma_cantharus -0.1284367755
## Umbrina_cirrosa -0.0758127682
## Xyrichtys_novacula 0.0252240534confint(Final7.1)## 2.5 % 97.5 % Estimate
## (Intercept) -1.85785019 -1.56999788 -1.71392404
## LengthStd 0.12326163 0.49570584 0.30948373
## Habitatdemersal -0.30866211 -0.04695402 -0.17780806
## Habitatpelagic-neritic 0.16269649 0.54696656 0.35483152
## ComImportmedium -0.26469073 -0.01063930 -0.13766501
## ComImportminor -0.27595462 0.08107091 -0.09744185
## MonitArea_km2 0.01429649 0.03256629 0.02343139
## Std.Dev.(Intercept)|Transmitter 0.22955978 0.25702674 0.24290533
## Std.Dev.(Intercept)|File 0.12905544 0.21406286 0.16621064#evaluate if the final model is better than the ones including only one biological trait
anova(Final7.1, glmmTMB(KUD50 ~ LengthStd + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Data: week_kuds
## Models:
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")): KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Final7.1: KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7.1: Transmitter) + (1 | File), zi=~0, disp=~1
## Df
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 5
## Final7.1 10
## AIC
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76335
## Final7.1 -76374
## BIC
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76294
## Final7.1 -76292
## logLik
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 38172
## Final7.1 38197
## deviance
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76345
## Final7.1 -76394
## Chisq
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 49.03
## Chi Df
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 5
## Pr(>Chisq)
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 2.188e-09
##
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(Final7.1, glmmTMB(KUD50 ~ BodyMassStd + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Data: week_kuds
## Models:
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")): KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Final7.1: KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7.1: Transmitter) + (1 | File), zi=~0, disp=~1
## Df
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 5
## Final7.1 10
## AIC
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76330
## Final7.1 -76374
## BIC
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76289
## Final7.1 -76292
## logLik
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 38170
## Final7.1 38197
## deviance
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76340
## Final7.1 -76394
## Chisq
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 53.535
## Chi Df
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 5
## Pr(>Chisq)
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 2.612e-10
##
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(Final7.1, glmmTMB(KUD50 ~ Habitat + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Data: week_kuds
## Models:
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")): KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Final7.1: KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7.1: Transmitter) + (1 | File), zi=~0, disp=~1
## Df
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 6
## Final7.1 10
## AIC
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76344
## Final7.1 -76374
## BIC
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76295
## Final7.1 -76292
## logLik
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 38178
## Final7.1 38197
## deviance
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76356
## Final7.1 -76394
## Chisq
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 37.999
## Chi Df
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 4
## Pr(>Chisq)
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 1.121e-07
##
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(Final7.1, glmmTMB(KUD50 ~ Migration + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log")))## Data: week_kuds
## Models:
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")): KUD50 ~ Migration + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## Final7.1: KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7.1: Transmitter) + (1 | File), zi=~0, disp=~1
## Df
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 5
## Final7.1 10
## AIC
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76333
## Final7.1 -76374
## BIC
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76292
## Final7.1 -76292
## logLik
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) 38171
## Final7.1 38197
## deviance
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log")) -76343
## Final7.1 -76394
## Chisq
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 51.106
## Chi Df
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 5
## Pr(>Chisq)
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 8.225e-10
##
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | File), data = week_kuds, family = Gamma(link = "log"))
## Final7.1 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1We reached the best fitted model. Now we must analyse the residuals to see how they behave.
#Test residuals for the best model (goodness of fit)
testDispersion(Final7.1) #plot with normality, dispersion and outliers##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.5106, p-value < 2.2e-16
## alternative hypothesis: two.sidedsimulationOutput1 <- simulateResiduals(fittedModel = Final7.1, plot = F) #dispersion test
testDispersion(simulationOutput1) #dispersion test
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.5106, p-value < 2.2e-16
## alternative hypothesis: two.sidedplot(simulationOutput1) #residual analysis
plotQQunif(simulationOutput1) #Q-Q plot (normality checking)
plotResiduals(simulationOutput1) #residual vs. predicted (homoscedasticity checking)
testOutliers(simulationOutput1) #outliers checking
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: simulationOutput1
## outliers at both margin(s) = 238, observations = 25612, p-value =
## 0.01853
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
## 0.008153861 0.010544498
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 )
## 0.009292519#Simulations from the model
getObservedResponse(Final7.1) #response used to fit the model## [1] 0.216 0.180 0.575 0.176 0.185 0.165 0.171 0.207 0.192 0.361 0.227 0.340
## [13] 0.469 0.295 0.165 0.165 0.189 0.222 0.245 0.251 0.256 0.219 0.241 0.187
## [25] 0.255 0.190 0.189 0.188 0.190 0.181 0.473 0.490 0.510 0.360 0.528 0.463
## [37] 0.469 0.521 0.444 0.467 0.477 0.494 0.308 0.393 0.463 0.480 0.469 0.490
## [49] 0.456 0.417 0.345 0.209 0.274 0.423 0.422 0.470 0.388 0.478 0.426 0.270
## [61] 0.309 0.262 0.441 0.466 0.273 0.334 0.458 0.614 0.454 0.464 0.433 0.503
## [73] 0.467 0.428 0.385 0.326 0.347 0.395 0.349 0.395 0.474 0.405 0.436 0.458
## [85] 0.188 0.406 0.240 0.216 0.192 0.210 0.199 0.183 0.199 0.198 0.200 0.202
## [97] 0.232 0.205 0.182 0.184 0.182 0.173 0.180 0.185 0.180 0.185 0.177 0.178
## [109] 0.176 0.219 0.277 0.243 0.232 0.248 0.225 0.257 0.209 0.210 0.294 0.204
## [121] 0.191 0.189 0.246 0.243 0.195 0.195 0.186 0.280 0.209 0.222 0.190 0.188
## [133] 0.207 0.290 0.374 0.210 0.364 0.300 0.206 0.180 0.203 0.211 0.213 0.183
## [145] 0.237 0.207 0.231 0.257 0.235 0.210 0.203 0.252 0.232 0.224 0.246 0.257
## [157] 0.194 0.277 0.250 0.249 0.281 0.266 0.189 0.287 0.289 0.279 0.273 0.288
## [169] 0.276 0.266 0.251 0.193 0.180 0.198 0.272 0.230 0.184 0.169 0.187 0.293
## [181] 0.311 0.317 0.270 0.188 0.243 0.375 0.245 0.216 0.340 0.454 0.194 0.264
## [193] 0.197 0.205 0.284 0.215 0.273 0.177 0.196 0.176 0.203 0.229 0.222 0.201
## [205] 0.196 0.210 0.202 0.271 0.193 0.220 0.226 0.231 0.234 0.235 0.232 0.200
## [217] 0.252 0.408 0.556 0.340 0.211 0.192 0.497 0.365 0.196 0.194 0.216 0.473
## [229] 0.337 0.380 0.468 0.358 0.283 0.426 0.269 0.200 0.285 0.229 0.302 0.209
## [241] 0.214 0.199 0.181 0.193 0.183 0.171 0.172 0.175 0.201 0.170 0.169 0.170
## [253] 0.193 0.195 0.223 0.211 0.254 0.260 0.339 0.286 0.259 0.250 0.269 0.252
## [265] 0.292 0.325 0.245 0.237 0.210 0.389 0.277 0.218 0.219 0.256 0.376 0.367
## [277] 0.330 0.401 0.279 0.230 0.278 0.258 0.196 0.246 0.290 0.240 0.205 0.188
## [289] 0.192 0.189 0.177 0.245 0.199 0.225 0.218 0.245 0.270 0.247 0.225 0.273
## [301] 0.272 0.263 0.255 0.281 0.258 0.251 0.261 0.243 0.245 0.220 0.221 0.232
## [313] 0.234 0.226 0.198 0.211 0.227 0.257 0.259 0.260 0.275 0.234 0.265 0.172
## [325] 0.189 0.186 0.248 0.234 0.269 0.233 0.210 0.235 0.348 0.249 0.279 0.249
## [337] 0.233 0.236 0.208 0.290 0.232 0.209 0.291 0.252 0.337 0.271 0.343 0.301
## [349] 0.344 0.273 0.321 0.329 0.483 0.455 0.365 0.467 0.574 0.241 0.314 0.591
## [361] 0.588 0.374 0.279 0.230 0.574 0.435 0.545 0.314 0.737 0.341 0.414 0.500
## [373] 0.234 0.349 0.249 0.248 0.220 0.350 0.184 0.177 0.206 0.212 0.279 0.196
## [385] 0.307 0.172 0.169 0.166 0.168 0.165 0.168 0.167 0.167 0.167 0.168 0.266
## [397] 0.220 0.186 0.251 0.276 0.191 0.183 0.196 0.219 0.199 0.174 0.192 0.233
## [409] 0.209 0.200 0.261 0.239 0.311 0.217 0.271 0.271 0.303 0.281 0.247 0.225
## [421] 0.299 0.291 0.234 0.221 0.232 0.242 0.201 0.265 0.191 0.190 0.187 0.181
## [433] 0.185 0.178 0.175 0.173 0.176 0.200 0.189 0.177 0.176 0.177 0.172 0.171
## [445] 0.173 0.172 0.179 0.186 0.199 0.195 0.175 0.189 0.212 0.212 0.314 0.249
## [457] 0.218 0.217 0.210 0.350 0.172 0.177 0.192 0.190 0.187 0.195 0.201 0.190
## [469] 0.178 0.191 0.201 0.197 0.187 0.178 0.222 0.185 0.187 0.176 0.201 0.182
## [481] 0.211 0.190 0.225 0.253 0.217 0.218 0.272 0.216 0.211 0.198 0.191 0.217
## [493] 0.187 0.167 0.173 0.248 0.224 0.185 0.167 0.170 0.292 0.287 0.276 0.219
## [505] 0.188 0.227 0.408 0.319 0.200 0.400 0.276 0.238 0.287 0.194 0.234 0.213
## [517] 0.215 0.170 0.175 0.282 0.316 0.269 0.293 0.269 0.240 0.225 0.212 0.222
## [529] 0.264 0.293 0.267 0.222 0.298 0.302 0.323 0.315 0.277 0.282 0.332 0.312
## [541] 0.179 0.242 0.299 0.334 0.435 0.292 0.321 0.338 0.383 0.241 0.332 0.307
## [553] 0.316 0.240 0.300 0.309 0.289 0.252 0.319 0.295 0.307 0.276 0.326 0.288
## [565] 0.271 0.275 0.289 0.285 0.305 0.250 0.263 0.371 0.178 0.174 0.172 0.170
## [577] 0.171 0.171 0.176 0.174 0.169 0.169 0.171 0.172 0.173 0.175 0.170 0.189
## [589] 0.196 0.183 0.179 0.180 0.174 0.172 0.181 0.171 0.171 0.170 0.173 0.176
## [601] 0.168 0.167 0.172 0.180 0.167 0.167 0.168 0.168 0.168 0.168 0.174 0.166
## [613] 0.172 0.167 0.169 0.169 0.171 0.187 0.170 0.173 0.173 0.169 0.175 0.174
## [625] 0.170 0.261 0.252 0.270 0.271 0.260 0.277 0.261 0.269 0.279 0.283 0.275
## [637] 0.274 0.265 0.282 0.271 0.276 0.275 0.283 0.273 0.276 0.275 0.286 0.232
## [649] 0.236 0.228 0.236 0.365 0.262 0.316 0.381 0.293 0.278 0.272 0.242 0.265
## [661] 0.341 0.370 0.352 0.252 0.282 0.289 0.308 0.259 0.267 0.307 0.182 0.221
## [673] 0.254 0.278 0.263 0.270 0.178 0.172 0.177 0.170 0.175 0.177 0.188 0.175
## [685] 0.171 0.173 0.171 0.171 0.183 0.172 0.169 0.180 0.176 0.173 0.177 0.199
## [697] 0.252 0.170 0.215 0.268 0.292 0.273 0.386 0.174 0.169 0.175 0.180 0.174
## [709] 0.171 0.295 0.295 0.337 0.330 0.276 0.279 0.344 0.432 0.298 0.275 0.334
## [721] 0.401 0.365 0.287 0.381 0.333 0.390 0.289 0.254 0.270 0.330 0.365 0.255
## [733] 0.215 0.239 0.260 0.350 0.377 0.251 0.229 0.338 0.326 0.285 0.302 0.252
## [745] 0.251 0.251 0.349 0.286 0.218 0.280 0.245 0.222 0.244 0.299 0.393 0.255
## [757] 0.297 0.296 0.464 0.370 0.429 0.457 0.407 0.391 0.293 0.282 0.405 0.414
## [769] 0.401 0.373 0.384 0.393 0.417 0.393 0.488 0.431 0.247 0.271 0.422 0.522
## [781] 0.488 0.367 0.185 0.269 0.479 0.318 0.204 0.394 0.200 0.207 0.176 0.200
## [793] 0.201 0.276 0.360 0.324 0.371 0.318 0.232 0.364 0.390 0.366 0.286 0.220
## [805] 0.360 0.387 0.222 0.201 0.187 0.203 0.237 0.199 0.172 0.174 0.189 0.169
## [817] 0.230 0.182 0.165 0.165 0.258 0.177 0.330 0.457 0.385 0.321 0.356 0.492
## [829] 0.438 0.307 0.264 0.416 0.479 0.363 0.272 0.569 0.337 0.249 0.248 0.291
## [841] 0.241 0.447 0.290 0.195 0.185 0.211 0.256 0.345 0.187 0.283 0.428 0.426
## [853] 0.445 0.361 0.418 0.532 0.466 0.396 0.244 0.247 0.274 0.214 0.183 0.180
## [865] 0.424 0.284 0.377 0.222 0.400 0.265 0.264 0.242 0.169 0.180 0.164 0.165
## [877] 0.165 0.165 0.165 0.223 0.259 0.252 0.268 0.279 0.281 0.242 0.269 0.297
## [889] 0.285 0.300 0.308 0.375 0.300 0.233 0.410 0.338 0.330 0.194 0.305 0.259
## [901] 0.248 0.324 0.199 0.333 0.165 0.165 0.165 0.164 0.165 0.162 0.162 0.165
## [913] 0.163 0.162 0.165 0.162 0.165 0.164 0.165 0.164 0.165 0.165 0.165 0.165
## [925] 0.166 0.164 0.375 0.233 0.181 0.223 0.311 0.235 0.304 0.386 0.276 0.297
## [937] 0.331 0.253 0.276 0.170 0.166 0.190 0.178 0.165 0.166 0.165 0.164 0.164
## [949] 0.164 0.164 0.164 0.165 0.164 0.164 0.162 0.188 0.189 0.197 0.219 0.206
## [961] 0.274 0.245 0.292 0.337 0.260 0.260 0.184 0.270 0.314 0.395 0.348 0.446
## [973] 0.510 0.292 0.168 0.265 0.194 0.176 0.379 0.418 0.396 0.307 0.320 0.397
## [985] 0.442 0.463 0.360 0.489 0.475 0.465 0.359 0.484 0.420 0.375 0.387 0.165
## [997] 0.260 0.283 0.222 0.265 0.190 0.193 0.531 0.177 0.220 0.233 0.251 0.190
## [1009] 0.271 0.175 0.331 0.316 0.337 0.253 0.260 0.245 0.214 0.235 0.190 0.183
## [1021] 0.171 0.180 0.181 0.199 0.182 0.312 0.328 0.258 0.338 0.237 0.323 0.329
## [1033] 0.328 0.164 0.597 0.199 0.203 0.215 0.328 0.227 0.298 0.303 0.325 0.278
## [1045] 0.224 0.195 0.162 0.162 0.165 0.162 0.165 0.164 0.164 0.165 0.165 0.165
## [1057] 0.164 0.165 0.530 0.385 0.162 0.164 0.164 0.165 0.165 0.163 0.164 0.163
## [1069] 0.162 0.165 0.162 0.163 0.165 0.163 0.164 0.164 0.164 0.164 0.164 0.165
## [1081] 0.165 0.164 0.165 0.164 0.165 0.164 0.165 0.165 0.165 0.165 0.164 0.162
## [1093] 0.165 0.162 0.164 0.163 0.165 0.311 0.339 0.227 0.338 0.165 0.265 0.204
## [1105] 0.209 0.192 0.232 0.172 0.174 0.172 0.193 0.194 0.207 0.222 0.248 0.212
## [1117] 0.563 0.344 0.536 0.437 0.616 0.377 0.595 0.414 0.492 0.502 0.519 0.538
## [1129] 0.579 0.359 0.382 0.369 0.440 0.402 0.474 0.574 1.394 1.494 1.843 0.560
## [1141] 0.477 0.626 0.999 0.792 1.046 0.981 0.709 0.857 0.385 0.264 0.383 0.347
## [1153] 0.252 0.256 0.236 0.182 0.195 0.191 0.176 0.171 0.208 0.232 0.224 0.303
## [1165] 0.222 0.220 0.184 0.298 0.254 0.219 0.293 0.405 0.272 0.498 0.361 0.582
## [1177] 0.344 0.555 0.272 0.500 0.221 0.402 0.277 0.574 0.278 0.568 0.334 0.449
## [1189] 0.438 0.583 0.375 0.568 0.370 0.548 0.413 0.429 0.435 0.379 0.477 0.420
## [1201] 0.294 0.346 0.463 0.473 0.426 0.424 0.248 0.437 0.219 0.336 0.207 0.359
## [1213] 0.230 0.427 0.242 0.408 0.372 0.974 0.859 0.566 0.369 0.374 0.274 0.346
## [1225] 0.285 0.527 0.617 0.271 0.247 0.270 0.252 0.211 0.184 0.223 0.202 0.192
## [1237] 0.215 0.208 0.223 0.242 0.200 0.258 0.185 0.233 0.283 0.225 0.319 0.188
## [1249] 0.224 0.207 0.208 0.271 0.332 0.326 0.395 0.450 0.514 0.364 0.390 0.306
## [1261] 0.356 0.441 0.314 0.399 0.367 0.389 0.291 0.334 0.309 0.384 0.447 0.490
## [1273] 0.463 0.368 0.529 0.258 0.349 0.433 0.258 0.395 0.236 0.275 0.232 0.232
## [1285] 0.259 0.231 0.302 0.234 0.276 0.240 0.248 0.260 0.272 0.371 0.278 0.283
## [1297] 0.245 0.247 0.311 0.244 0.294 0.287 0.428 0.453 0.449 0.478 0.686 0.611
## [1309] 0.459 0.529 0.346 0.336 0.281 0.220 0.184 0.287 0.209 0.278 0.210 0.218
## [1321] 0.233 0.297 0.280 0.193 0.190 0.217 0.246 0.350 0.290 0.273 0.305 0.194
## [1333] 0.224 0.198 0.257 0.268 0.262 0.345 0.223 0.327 0.278 0.276 0.310 0.281
## [1345] 0.239 0.285 0.226 0.352 0.245 0.294 0.253 0.334 0.329 0.268 0.461 0.292
## [1357] 0.328 0.234 0.251 0.255 0.201 0.171 0.229 0.207 0.301 0.280 0.273 0.219
## [1369] 0.252 0.199 0.329 0.251 0.325 0.301 0.408 0.260 0.181 0.180 0.195 0.168
## [1381] 0.176 0.195 0.283 0.284 0.344 0.317 0.320 0.316 0.254 0.326 0.241 0.305
## [1393] 0.179 0.164 0.164 0.165 0.165 0.164 0.180 0.165 0.165 0.168 0.193 0.164
## [1405] 0.164 0.163 0.164 0.163 0.165 0.164 0.164 0.164 0.183 0.164 0.167 0.164
## [1417] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.165 0.164
## [1429] 0.164 0.170 0.165 0.165 0.168 0.208 0.164 0.164 0.162 0.163 0.164 0.163
## [1441] 0.165 0.165 0.163 0.198 0.164 0.164 0.207 0.174 0.164 0.165 0.172 0.164
## [1453] 0.164 0.164 0.164 0.164 0.164 0.164 0.294 0.330 0.237 0.163 0.164 0.163
## [1465] 0.164 0.164 0.202 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.332 0.163
## [1477] 0.164 0.195 0.163 0.164 0.164 0.190 0.164 0.164 0.164 0.164 0.164 0.164
## [1489] 0.164 0.165 0.165 0.164 0.222 0.333 0.616 0.322 0.203 0.195 0.164 0.323
## [1501] 0.164 0.323 0.322 0.458 0.387 0.165 0.548 0.682 0.402 0.254 0.624 0.223
## [1513] 0.323 0.420 0.322 0.332 0.164 0.330 0.163 0.165 0.164 0.164 0.413 0.220
## [1525] 0.713 0.408 0.608 0.307 0.701 0.682 0.164 0.464 0.249 0.164 0.164 0.164
## [1537] 0.164 0.178 0.164 0.164 0.164 0.220 0.164 0.164 0.164 0.163 0.165 0.165
## [1549] 0.164 0.163 0.164 0.165 0.162 0.165 0.164 0.165 0.165 0.165 0.164 0.163
## [1561] 0.164 0.164 0.164 0.163 0.165 0.164 0.164 0.162 0.164 0.165 0.165 0.163
## [1573] 0.165 0.164 0.164 0.165 0.319 0.186 0.262 0.165 0.164 0.164 0.164 0.164
## [1585] 0.165 0.164 0.192 0.322 0.164 0.164 0.163 0.162 0.164 0.171 0.164 0.164
## [1597] 0.164 0.165 0.165 0.331 0.170 0.246 0.165 0.164 0.164 0.164 0.164 0.165
## [1609] 0.172 0.212 0.322 0.164 0.164 0.163 0.162 0.164 0.171 0.164 0.164 0.164
## [1621] 0.164 0.236 0.236 0.465 0.302 0.218 0.277 0.174 0.199 0.163 0.164 0.165
## [1633] 0.165 0.163 0.164 0.164 0.179 0.226 0.175 0.176 0.212 0.219 0.190 0.212
## [1645] 0.229 0.459 0.268 0.219 0.249 0.195 0.182 0.171 0.178 0.163 0.165 0.165
## [1657] 0.165 0.184 0.164 0.164 0.176 0.197 0.180 0.181 0.205 0.232 0.181 0.165
## [1669] 0.164 0.165 0.164 0.331 0.343 0.322 0.182 0.162 0.163 0.164 0.178 0.162
## [1681] 0.163 0.164 0.300 0.329 0.212 0.327 0.238 0.333 0.322 0.489 0.204 0.214
## [1693] 0.256 0.165 0.165 0.295 0.371 0.162 0.617 0.418 0.588 0.211 0.162 0.187
## [1705] 0.246 0.398 0.162 0.408 0.628 0.491 0.250 0.162 0.164 0.164 0.165 0.165
## [1717] 0.162 0.165 0.164 0.165 0.198 0.745 0.240 0.418 0.407 0.627 0.432 0.258
## [1729] 0.683 0.379 0.367 0.749 0.257 0.285 0.636 0.783 0.381 0.251 0.407 0.406
## [1741] 0.211 0.565 0.679 0.580 0.365 0.394 0.472 0.362 0.355 0.165 0.165 0.162
## [1753] 0.165 0.164 0.165 0.164 0.165 0.165 0.163 0.164 0.164 0.164 0.164 0.165
## [1765] 0.165 0.162 0.164 0.165 0.164 0.165 0.165 0.164 0.164 0.222 0.162 0.164
## [1777] 0.165 0.165 0.162 0.165 0.164 0.165 0.164 0.165 0.165 0.163 0.164 0.164
## [1789] 0.164 0.164 0.163 0.164 0.204 0.163 0.191 0.165 0.164 0.165 0.165 0.165
## [1801] 0.163 0.164 0.164 0.164 0.164 0.204 0.450 0.303 0.164 0.424 0.186 0.164
## [1813] 0.222 0.528 0.406 0.187 0.427 0.216 0.164 0.255 0.243 0.190 0.173 0.301
## [1825] 0.163 0.400 0.448 0.247 0.196 0.189 0.305 0.165 0.165 0.162 0.165 0.164
## [1837] 0.165 0.164 0.165 0.165 0.163 0.164 0.164 0.164 0.164 0.164 0.165 0.162
## [1849] 0.164 0.165 0.165 0.162 0.165 0.164 0.165 0.164 0.165 0.165 0.163 0.164
## [1861] 0.164 0.164 0.164 0.164 0.173 0.184 0.190 0.162 0.163 0.164 0.176 0.192
## [1873] 0.176 0.163 0.164 0.236 0.207 0.205 0.388 0.466 0.315 0.360 0.202 0.187
## [1885] 0.180 0.368 0.466 0.329 0.339 0.238 0.277 0.294 0.223 0.827 0.197 0.248
## [1897] 0.318 0.184 0.250 0.322 0.164 0.164 0.171 0.188 0.164 0.164 0.168 0.199
## [1909] 0.400 0.319 0.400 0.380 0.164 0.164 0.164 0.164 0.164 0.164 0.349 0.188
## [1921] 0.164 0.164 0.281 0.331 0.216 0.304 0.332 0.337 0.272 0.283 0.165 0.322
## [1933] 0.238 0.346 0.400 0.254 0.498 0.235 0.165 0.322 0.211 0.374 0.352 0.376
## [1945] 0.486 0.221 0.286 0.343 0.164 0.213 0.183 0.164 0.164 0.237 0.234 0.204
## [1957] 0.164 0.164 0.319 0.277 0.274 0.178 0.164 0.164 0.243 0.272 0.293 0.171
## [1969] 0.164 0.322 0.164 0.232 0.295 0.164 0.203 0.277 0.164 0.164 0.252 0.221
## [1981] 0.162 0.163 0.164 0.164 0.227 0.249 0.172 0.200 0.297 0.164 0.164 0.249
## [1993] 0.225 0.162 0.163 0.448 0.306 0.419 0.429 0.329 0.260 0.316 0.328 0.371
## [2005] 0.299 0.409 0.416 0.339 0.274 0.345 0.297 0.164 0.164 0.164 0.322 0.165
## [2017] 0.185 0.164 0.164 0.164 0.164 0.164 0.163 0.330 0.163 0.164 0.164 0.164
## [2029] 0.324 0.165 0.181 0.164 0.164 0.164 0.164 0.164 0.163 0.329 0.163 0.300
## [2041] 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.162 0.163
## [2053] 0.307 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.162
## [2065] 0.163 0.226 0.412 0.279 0.319 0.225 0.164 0.230 0.404 0.273 0.278 0.226
## [2077] 0.164 0.164 0.164 0.312 0.168 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [2089] 0.164 0.309 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165
## [2101] 0.164 0.164 0.164 0.165 0.164 0.318 0.204 0.193 0.191 0.189 0.171 0.327
## [2113] 0.218 0.194 0.197 0.183 0.166 0.265 0.254 0.210 0.273 0.301 0.269 0.329
## [2125] 0.313 0.330 0.251 0.215 0.183 0.267 0.204 0.331 0.237 0.235 0.253 0.215
## [2137] 0.273 0.273 0.252 0.326 0.299 0.331 0.254 0.226 0.188 0.254 0.212 0.330
## [2149] 0.293 0.192 0.489 0.202 0.165 0.164 0.164 0.632 0.167 0.182 0.166 0.177
## [2161] 0.169 0.259 0.284 0.173 0.174 0.194 0.172 0.263 0.195 0.171 0.428 0.205
## [2173] 0.185 0.165 0.268 0.356 0.224 0.187 0.173 0.194 0.250 0.173 0.231 0.197
## [2185] 0.528 0.177 0.165 0.164 0.748 0.172 0.164 0.178 0.190 0.179 0.343 0.291
## [2197] 0.175 0.173 0.199 0.179 0.236 0.190 0.168 0.456 0.189 0.184 0.165 0.243
## [2209] 0.348 0.198 0.180 0.166 0.198 0.230 0.192 0.333 0.226 0.328 0.332 0.200
## [2221] 0.333 0.211 0.252 0.320 0.164 0.218 0.288 0.239 0.330 0.326 0.269 0.333
## [2233] 0.219 0.330 0.330 0.206 0.332 0.217 0.253 0.323 0.164 0.220 0.261 0.252
## [2245] 0.329 0.331 0.269 0.162 1.049 0.171 0.165 0.269 0.235 0.168 0.202 0.620
## [2257] 0.444 0.164 0.226 0.164 0.169 0.669 0.582 0.620 0.602 0.192 0.767 0.272
## [2269] 0.172 0.833 0.734 0.523 0.623 0.319 0.165 0.382 0.503 0.331 0.168 0.164
## [2281] 0.165 0.164 0.265 0.362 0.349 0.294 0.185 0.227 0.403 0.221 0.441 0.331
## [2293] 0.445 0.456 0.288 0.187 0.251 0.492 0.244 0.347 0.297 0.479 0.272 0.308
## [2305] 0.306 0.485 0.642 0.304 0.164 0.575 0.489 0.164 0.199 0.164 1.126 0.394
## [2317] 0.736 0.686 0.337 0.343 0.471 0.337 0.184 0.724 0.251 0.534 0.611 0.379
## [2329] 1.186 0.295 0.259 0.322 0.439 0.484 0.460 0.343 0.248 0.268 0.164 0.173
## [2341] 0.742 0.196 0.257 0.347 0.817 0.164 0.165 0.920 1.098 0.698 0.229 0.442
## [2353] 0.165 0.467 0.165 0.254 0.164 0.319 0.361 0.835 0.357 0.779 0.389 0.721
## [2365] 0.781 0.776 0.443 0.870 0.799 0.736 0.424 0.165 0.165 0.285 0.267 0.170
## [2377] 0.276 0.343 0.165 0.247 0.182 0.179 0.194 0.164 0.165 0.165 0.283 0.187
## [2389] 0.165 0.165 0.165 0.165 0.164 0.165 0.165 0.164 0.171 0.165 0.164 0.209
## [2401] 0.162 0.308 0.168 0.164 0.164 0.163 0.325 0.164 0.165 0.164 0.165 0.164
## [2413] 0.165 0.165 0.165 0.165 0.164 0.165 0.164 0.165 0.164 0.165 0.165 0.164
## [2425] 0.165 0.165 0.165 0.165 0.664 0.224 0.671 0.165 0.164 0.427 0.404 0.275
## [2437] 0.361 0.310 0.441 0.507 0.740 0.694 0.666 0.627 0.528 0.432 0.362 0.425
## [2449] 0.345 0.403 0.407 0.464 0.472 0.410 0.381 0.519 0.314 0.199 0.163 0.164
## [2461] 0.453 0.220 0.239 0.236 0.498 0.287 0.266 0.273 0.245 0.216 0.684 0.163
## [2473] 0.164 0.528 0.283 1.271 0.204 1.429 0.164 0.204 0.201 0.239 0.620 0.389
## [2485] 0.366 0.321 0.310 0.164 0.959 0.718 0.211 0.316 0.427 0.685 0.163 0.550
## [2497] 1.510 0.457 0.665 0.229 0.374 0.431 0.392 0.402 0.338 0.374 0.419 0.589
## [2509] 0.455 0.360 0.509 0.235 0.388 0.869 0.268 0.684 0.545 0.400 0.345 0.388
## [2521] 0.469 0.287 0.165 0.232 0.741 0.840 0.668 1.089 1.092 0.994 0.163 0.164
## [2533] 0.211 0.164 0.543 0.330 0.524 0.903 0.370 0.536 0.948 0.274 0.349 0.406
## [2545] 0.245 0.372 0.405 0.205 0.311 0.201 0.379 0.296 0.439 0.170 0.370 0.281
## [2557] 0.375 0.193 0.419 0.257 0.212 0.298 0.283 0.415 0.293 0.429 0.374 0.285
## [2569] 0.369 0.331 0.266 0.448 0.342 0.354 0.191 0.339 0.165 0.236 0.171 0.360
## [2581] 0.165 0.215 0.165 0.218 0.165 0.330 0.408 0.165 0.344 0.165 0.430 0.458
## [2593] 0.221 0.406 0.406 0.351 0.369 0.303 0.431 0.338 0.377 0.340 0.340 0.284
## [2605] 0.402 0.381 0.530 0.350 0.449 0.486 0.478 0.362 0.321 0.233 0.312 0.398
## [2617] 0.165 0.163 0.162 0.164 0.164 0.164 0.163 0.164 0.163 0.164 0.164 0.164
## [2629] 0.164 0.164 0.165 0.163 0.217 0.327 0.185 0.163 0.162 0.164 0.164 0.164
## [2641] 0.165 0.297 1.148 0.564 0.458 0.503 0.540 0.614 0.686 0.327 0.474 0.491
## [2653] 0.259 0.248 0.225 0.397 0.231 0.209 0.199 0.245 0.443 0.327 0.165 0.164
## [2665] 0.164 0.162 0.164 0.164 0.164 0.164 0.165 0.164 0.162 0.165 0.164 0.165
## [2677] 0.164 0.165 0.163 0.165 0.169 0.206 0.174 0.167 0.165 0.164 0.166 0.165
## [2689] 0.199 0.164 0.165 0.164 0.177 0.164 0.163 0.168 0.164 0.164 0.199 0.203
## [2701] 0.183 0.215 0.176 0.165 0.162 0.163 0.164 0.164 0.164 0.164 0.165 0.163
## [2713] 0.164 0.164 0.804 1.049 0.423 1.496 0.959 1.009 0.511 1.330 1.166 1.510
## [2725] 2.561 0.809 1.382 0.565 0.711 0.645 3.104 0.564 2.383 0.487 1.296 0.465
## [2737] 0.506 1.168 1.557 1.180 0.613 0.941 0.815 0.274 0.525 0.377 0.423 0.376
## [2749] 0.381 0.370 0.382 0.388 0.364 0.378 0.379 0.455 0.526 0.419 0.256 0.292
## [2761] 0.295 0.315 0.244 0.337 0.361 0.447 0.378 0.338 0.423 0.384 0.337 0.350
## [2773] 0.452 0.416 0.353 0.646 0.449 0.354 0.617 0.377 0.426 0.304 0.353 0.513
## [2785] 0.313 0.632 0.494 0.466 0.903 0.522 0.602 0.702 0.442 0.437 0.667 0.693
## [2797] 0.666 0.564 0.470 0.717 0.691 0.914 0.767 0.741 0.582 0.526 0.819 0.270
## [2809] 0.712 1.441 0.480 1.140 0.758 0.528 0.366 0.519 0.690 0.458 0.882 0.663
## [2821] 0.715 0.806 0.615 0.509 0.635 0.549 0.556 0.562 0.637 0.705 0.604 0.524
## [2833] 0.831 0.352 0.430 0.239 0.402 0.276 0.288 0.250 0.273 0.261 0.377 0.415
## [2845] 0.348 0.557 0.407 0.710 0.465 0.625 0.398 0.715 0.423 0.505 0.615 0.911
## [2857] 0.326 0.739 0.734 0.778 0.668 0.546 0.641 0.618 0.479 0.709 0.565 0.497
## [2869] 0.734 0.542 1.003 0.486 0.838 0.177 0.292 0.615 0.182 0.332 0.320 0.311
## [2881] 0.519 0.389 0.333 0.379 0.376 0.345 0.345 0.404 0.406 0.374 0.342 0.304
## [2893] 0.374 0.267 0.379 0.382 1.377 0.832 0.988 0.310 0.353 0.390 0.399 0.374
## [2905] 0.328 0.361 0.337 0.301 0.312 0.334 0.397 0.164 0.165 0.165 0.165 0.165
## [2917] 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.232 0.255 0.172
## [2929] 0.268 0.316 0.320 0.232 0.333 0.165 0.259 0.165 0.322 0.296 0.182 0.165
## [2941] 0.248 0.233 0.238 0.479 0.371 0.278 0.329 0.394 0.217 0.217 0.234 0.259
## [2953] 0.247 0.294 0.244 0.278 0.365 0.218 0.274 0.291 0.206 0.314 0.278 0.369
## [2965] 0.351 0.328 0.372 0.353 0.230 0.216 0.165 0.325 0.341 0.308 0.282 0.194
## [2977] 0.325 0.328 0.290 0.279 0.226 0.394 0.197 0.164 0.285 0.451 0.264 0.251
## [2989] 0.164 0.164 0.164 0.310 0.378 0.503 0.388 0.512 0.177 0.185 0.178 0.164
## [3001] 0.164 0.164 0.167 0.166 0.166 0.167 0.164 0.164 0.164 0.165 0.173 0.238
## [3013] 0.239 0.223 0.220 0.229 0.177 0.238 0.194 0.210 0.219 0.224 0.221 0.166
## [3025] 0.213 0.167 0.168 0.173 0.166 0.165 0.166 0.169 0.171 0.166 0.167 0.166
## [3037] 0.166 0.170 0.175 0.174 0.170 0.169 0.179 0.167 0.166 0.165 0.165 0.165
## [3049] 0.165 0.242 0.243 0.236 0.204 0.212 0.242 0.265 0.256 0.340 0.348 0.237
## [3061] 0.357 0.284 0.416 0.163 0.165 0.258 0.232 0.248 0.247 0.245 0.258 0.283
## [3073] 0.259 0.265 0.203 0.302 0.302 0.274 0.248 0.384 0.243 0.221 0.464 0.464
## [3085] 0.479 0.398 0.356 0.216 0.219 0.192 0.253 0.240 0.234 0.164 0.166 0.164
## [3097] 0.222 0.176 0.166 0.173 0.177 0.194 0.210 0.192 0.196 0.204 0.230 0.221
## [3109] 0.214 0.173 0.175 0.260 0.200 0.180 0.166 0.164 0.164 0.165 0.164 0.165
## [3121] 0.164 0.164 0.164 0.164 0.164 0.165 0.163 0.164 0.163 0.163 0.163 0.168
## [3133] 0.174 0.164 0.164 0.164 0.165 0.167 0.172 0.167 0.182 0.167 0.172 0.211
## [3145] 0.225 0.218 0.221 0.227 0.239 0.305 0.335 0.214 0.325 0.200 0.362 0.170
## [3157] 0.162 0.181 0.164 0.169 0.164 0.163 0.164 0.164 0.163 0.164 0.163 0.166
## [3169] 0.165 0.163 0.163 0.163 0.163 0.165 0.164 0.171 0.164 0.164 0.165 0.166
## [3181] 0.172 0.164 0.186 0.186 0.164 0.164 0.167 0.166 0.166 0.165 0.182 0.176
## [3193] 0.165 0.164 0.176 0.187 0.190 0.185 0.187 0.186 0.187 0.191 0.191 0.179
## [3205] 0.185 0.185 0.184 0.180 0.181 0.190 0.180 0.193 0.269 0.242 0.311 0.168
## [3217] 0.189 0.177 0.193 0.168 0.181 0.165 0.165 0.165 0.165 0.165 0.165 0.194
## [3229] 0.173 0.165 0.165 0.164 0.205 0.165 0.215 0.164 0.268 0.262 0.220 0.164
## [3241] 0.176 0.177 0.180 0.186 0.173 0.184 0.213 0.165 0.193 0.181 0.186 0.193
## [3253] 0.208 0.200 0.165 0.165 0.183 0.181 0.170 0.164 0.191 0.237 0.203 0.509
## [3265] 0.287 0.508 0.196 0.766 0.516 0.645 0.244 0.502 0.322 0.556 0.227 0.442
## [3277] 0.298 0.514 0.381 0.500 0.336 0.603 0.316 0.325 0.164 0.233 0.383 0.398
## [3289] 0.164 0.207 0.164 0.164 0.227 0.204 0.165 0.457 0.422 0.234 0.482 0.693
## [3301] 0.580 0.374 0.325 0.453 0.356 0.186 0.396 0.445 0.164 0.245 0.344 0.256
## [3313] 0.334 0.190 0.205 0.165 0.204 0.164 0.165 0.221 0.199 0.165 0.164 0.174
## [3325] 0.164 0.165 0.165 0.165 0.165 0.306 0.238 0.207 0.290 0.212 0.196 0.178
## [3337] 0.175 0.202 0.191 0.179 0.175 0.214 0.250 0.186 0.257 0.274 0.257 0.220
## [3349] 0.207 0.499 0.357 0.217 0.225 0.230 0.261 0.232 0.221 0.248 0.221 0.189
## [3361] 0.229 0.193 0.200 0.181 0.164 0.165 0.164 0.172 0.182 0.165 0.169 0.164
## [3373] 0.164 0.296 0.205 0.192 0.204 0.268 0.203 0.184 0.183 0.192 0.188 0.197
## [3385] 0.198 0.215 0.202 0.200 0.224 0.203 0.216 0.218 0.262 0.218 0.221 0.316
## [3397] 0.212 0.262 0.274 0.250 0.279 0.254 0.235 0.232 0.227 0.173 0.183 0.168
## [3409] 0.172 0.217 0.220 0.262 0.304 0.295 0.229 0.406 0.245 0.332 0.325 0.302
## [3421] 0.224 0.246 0.277 0.250 0.230 0.187 0.232 0.238 0.235 0.202 0.200 0.179
## [3433] 0.184 0.170 0.172 0.172 0.171 0.177 0.174 0.165 0.174 0.164 0.348 0.228
## [3445] 0.164 0.213 0.245 0.189 0.254 0.168 0.209 0.170 0.250 0.244 0.235 0.194
## [3457] 0.217 0.237 0.228 0.207 0.238 0.162 0.208 0.241 0.230 0.198 0.242 0.206
## [3469] 0.171 0.177 0.171 0.173 0.219 0.213 0.188 0.174 0.175 0.164 0.178 0.164
## [3481] 0.243 0.237 0.225 0.177 0.235 0.164 0.257 0.362 0.325 0.329 0.384 0.323
## [3493] 0.321 0.270 0.289 0.351 0.323 0.245 0.336 0.332 0.321 0.277 0.420 0.300
## [3505] 0.465 0.335 0.325 0.189 0.249 0.305 0.311 0.313 0.288 0.263 0.287 0.271
## [3517] 0.274 0.244 0.299 0.330 0.191 0.164 0.164 0.164 0.164 0.164 0.164 0.185
## [3529] 0.164 0.164 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3541] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.229
## [3553] 0.165 0.165 0.165 0.259 0.165 0.165 0.165 0.165 0.347 0.165 0.165 0.165
## [3565] 0.414 0.165 0.165 0.216 0.165 0.165 0.192 0.165 0.203 0.165 0.165 0.165
## [3577] 0.165 0.354 0.165 0.165 0.165 0.334 0.165 0.165 0.165 0.165 0.165 0.210
## [3589] 0.165 0.165 0.165 0.437 0.165 0.165 0.374 0.165 0.165 0.165 0.178 0.165
## [3601] 0.165 0.165 0.165 0.165 0.165 0.177 0.165 0.165 0.165 0.165 0.165 0.165
## [3613] 0.165 0.165 0.323 0.165 0.165 0.165 0.178 0.165 0.165 0.165 0.263 0.165
## [3625] 0.165 0.165 0.165 0.195 0.165 0.165 0.175 0.165 0.165 0.165 0.178 0.165
## [3637] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3649] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3661] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3673] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3685] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.303 0.226
## [3697] 0.182 0.165 0.165 0.165 0.214 0.165 0.189 0.165 0.165 0.165 0.249 0.165
## [3709] 0.210 0.165 0.249 0.165 0.316 0.165 0.287 0.165 0.271 0.165 0.165 0.304
## [3721] 0.165 0.165 0.231 0.165 0.165 0.165 0.222 0.165 0.165 0.203 0.165 0.165
## [3733] 0.205 0.165 0.165 0.180 0.165 0.165 0.165 0.190 0.165 0.165 0.165 0.164
## [3745] 0.165 0.165 0.165 0.163 0.165 0.165 0.165 0.163 0.165 0.165 0.165 0.163
## [3757] 0.165 0.165 0.163 0.165 0.165 0.165 0.163 0.165 0.165 0.165 0.165 0.165
## [3769] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3781] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3793] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3805] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3817] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [3829] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.199 0.246
## [3841] 0.240 0.222 0.210 0.199 0.174 0.174 0.170 0.177 0.187 0.168 0.164 0.164
## [3853] 0.167 0.178 0.197 0.207 0.186 0.175 0.219 0.228 0.227 0.165 0.172 0.167
## [3865] 0.169 0.166 0.167 0.168 0.178 0.173 0.165 0.164 0.164 0.164 0.224 0.313
## [3877] 0.293 0.245 0.191 0.246 0.296 0.268 0.265 0.294 0.243 0.263 0.316 0.220
## [3889] 0.298 0.305 0.282 0.334 0.186 0.307 0.268 0.239 0.188 0.169 0.177 0.186
## [3901] 0.175 0.191 0.193 0.167 0.164 0.164 0.168 0.164 0.164 0.170 0.164 0.164
## [3913] 0.164 0.164 0.215 0.194 0.214 0.271 0.230 0.332 0.244 0.220 0.189 0.188
## [3925] 0.168 0.181 0.216 0.207 0.197 0.184 0.233 0.285 0.205 0.332 0.182 0.238
## [3937] 0.227 0.288 0.327 0.325 0.290 0.336 0.340 0.256 0.259 0.218 0.260 0.335
## [3949] 0.343 0.287 0.353 0.339 0.317 0.273 0.253 0.231 0.192 0.170 0.336 0.334
## [3961] 0.253 0.268 0.272 0.290 0.283 0.291 0.173 0.179 0.190 0.192 0.183 0.169
## [3973] 0.173 0.166 0.164 0.164 0.164 0.164 0.172 0.164 0.164 0.247 0.204 0.230
## [3985] 0.164 0.164 0.240 0.199 0.164 0.168 0.164 0.170 0.163 0.163 0.163 0.165
## [3997] 0.168 0.176 0.163 0.163 0.163 0.170 0.172 0.165 0.165 0.192 0.202 0.166
## [4009] 0.169 0.188 0.164 0.168 0.180 0.183 0.182 0.184 0.185 0.187 0.176 0.171
## [4021] 0.176 0.174 0.195 0.184 0.179 0.187 0.206 0.220 0.191 0.180 0.166 0.178
## [4033] 0.172 0.168 0.180 0.183 0.182 0.184 0.185 0.187 0.176 0.171 0.176 0.174
## [4045] 0.195 0.181 0.169 0.215 0.187 0.192 0.164 0.164 0.164 0.164 0.165 0.164
## [4057] 0.164 0.164 0.164 0.164 0.164 0.164 0.167 0.162 0.298 0.247 0.270 0.168
## [4069] 0.163 0.167 0.167 0.193 0.241 0.166 0.172 0.172 0.210 0.178 0.177 0.188
## [4081] 0.227 0.169 0.228 0.218 0.222 0.171 0.172 0.174 0.177 0.165 0.171 0.201
## [4093] 0.170 0.179 0.207 0.172 0.170 0.169 0.180 0.181 0.168 0.168 0.176 0.172
## [4105] 0.172 0.173 0.170 0.166 0.175 0.174 0.173 0.167 0.173 0.163 0.167 0.184
## [4117] 0.269 0.170 0.170 0.164 0.195 0.164 0.229 0.214 0.198 0.186 0.199 0.204
## [4129] 0.222 0.221 0.215 0.245 0.219 0.204 0.224 0.220 0.247 0.221 0.232 0.240
## [4141] 0.232 0.220 0.218 0.200 0.232 0.248 0.261 0.231 0.240 0.227 0.225 0.241
## [4153] 0.233 0.225 0.219 0.228 0.249 0.227 0.233 0.244 0.240 0.233 0.240 0.255
## [4165] 0.238 0.236 0.249 0.263 0.244 0.237 0.237 0.245 0.222 0.257 0.211 0.240
## [4177] 0.207 0.228 0.385 0.273 0.253 0.206 0.164 0.164 0.169 0.290 0.349 0.338
## [4189] 0.399 0.300 0.337 0.332 0.227 0.244 0.279 0.240 0.214 0.239 0.255 0.241
## [4201] 0.248 0.257 0.220 0.207 0.226 0.213 0.234 0.200 0.223 0.239 0.209 0.225
## [4213] 0.233 0.226 0.221 0.217 0.318 0.308 0.290 0.280 0.245 0.188 0.201 0.225
## [4225] 0.222 0.216 0.206 0.251 0.248 0.211 0.180 0.178 0.178 0.176 0.188 0.182
## [4237] 0.172 0.167 0.172 0.181 0.178 0.184 0.172 0.178 0.194 0.185 0.176 0.174
## [4249] 0.174 0.173 0.180 0.197 0.185 0.196 0.176 0.194 0.195 0.200 0.203 0.246
## [4261] 0.221 0.227 0.222 0.215 0.213 0.230 0.278 0.246 0.251 0.217 0.221 0.241
## [4273] 0.231 0.252 0.205 0.206 0.247 0.256 0.245 0.230 0.207 0.233 0.260 0.260
## [4285] 0.238 0.251 0.230 0.252 0.269 0.275 0.272 0.261 0.236 0.246 0.231 0.256
## [4297] 0.233 0.233 0.226 0.228 0.247 0.212 0.190 0.194 0.207 0.199 0.190 0.214
## [4309] 0.213 0.237 0.242 0.240 0.260 0.246 0.276 0.249 0.263 0.254 0.282 0.271
## [4321] 0.237 0.261 0.260 0.258 0.249 0.249 0.260 0.249 0.232 0.235 0.249 0.223
## [4333] 0.234 0.222 0.196 0.256 0.260 0.208 0.253 0.276 0.187 0.182 0.182 0.171
## [4345] 0.169 0.164 0.171 0.270 0.174 0.184 0.205 0.244 0.184 0.239 0.229 0.263
## [4357] 0.246 0.222 0.284 0.270 0.263 0.268 0.209 0.265 0.230 0.244 0.276 0.228
## [4369] 0.252 0.232 0.211 0.248 0.249 0.283 0.259 0.263 0.302 0.227 0.247 0.269
## [4381] 0.236 0.266 0.211 0.254 0.272 0.252 0.283 0.196 0.254 0.259 0.256 0.257
## [4393] 0.272 0.218 0.269 0.279 0.242 0.281 0.272 0.290 0.234 0.204 0.270 0.255
## [4405] 0.257 0.269 0.241 0.227 0.262 0.250 0.261 0.262 0.232 0.231 0.252 0.258
## [4417] 0.241 0.219 0.262 0.193 0.164 0.163 0.164 0.164 0.165 0.164 0.164 0.168
## [4429] 0.172 0.166 0.169 0.164 0.164 0.164 0.164 0.163 0.165 0.165 0.209 0.240
## [4441] 0.218 0.232 0.249 0.219 0.244 0.245 0.248 0.277 0.255 0.247 0.242 0.261
## [4453] 0.249 0.235 0.237 0.240 0.230 0.253 0.238 0.216 0.235 0.228 0.211 0.243
## [4465] 0.212 0.205 0.213 0.168 0.165 0.182 0.165 0.186 0.227 0.185 0.192 0.215
## [4477] 0.222 0.183 0.181 0.183 0.223 0.204 0.197 0.197 0.241 0.170 0.211 0.196
## [4489] 0.222 0.173 0.214 0.219 0.238 0.208 0.207 0.184 0.182 0.165 0.176 0.186
## [4501] 0.183 0.164 0.164 0.165 0.185 0.182 0.201 0.215 0.187 0.171 0.180 0.185
## [4513] 0.184 0.212 0.203 0.245 0.197 0.274 0.298 0.251 0.177 0.232 0.234 0.182
## [4525] 0.171 0.190 0.208 0.188 0.182 0.200 0.179 0.195 0.199 0.226 0.282 0.242
## [4537] 0.231 0.239 0.198 0.186 0.190 0.208 0.184 0.176 0.179 0.192 0.225 0.196
## [4549] 0.186 0.175 0.174 0.183 0.193 0.183 0.217 0.166 0.182 0.204 0.254 0.319
## [4561] 0.226 0.238 0.302 0.243 0.290 0.238 0.190 0.342 0.254 0.415 0.334 0.418
## [4573] 0.341 0.252 0.344 0.433 0.404 0.380 0.302 0.198 0.281 0.320 0.315 0.296
## [4585] 0.290 0.291 0.314 0.319 0.324 0.235 0.294 0.406 0.320 0.343 0.319 0.341
## [4597] 0.317 0.228 0.240 0.314 0.390 0.378 0.342 0.337 0.344 0.318 0.281 0.294
## [4609] 0.246 0.322 0.369 0.314 0.335 0.308 0.266 0.268 0.255 0.194 0.217 0.217
## [4621] 0.225 0.178 0.317 0.223 0.252 0.224 0.253 0.264 0.242 0.174 0.178 0.209
## [4633] 0.188 0.181 0.190 0.238 0.182 0.192 0.192 0.205 0.193 0.187 0.184 0.217
## [4645] 0.232 0.210 0.191 0.188 0.198 0.208 0.232 0.190 0.182 0.207 0.185 0.247
## [4657] 0.195 0.198 0.172 0.223 0.191 0.196 0.179 0.258 0.245 0.195 0.280 0.199
## [4669] 0.187 0.243 0.236 0.203 0.223 0.249 0.232 0.222 0.237 0.215 0.236 0.245
## [4681] 0.259 0.234 0.225 0.255 0.221 0.237 0.188 0.234 0.222 0.223 0.216 0.211
## [4693] 0.207 0.209 0.196 0.205 0.182 0.195 0.214 0.214 0.181 0.219 0.219 0.220
## [4705] 0.228 0.265 0.223 0.221 0.227 0.225 0.193 0.218 0.221 0.215 0.202 0.238
## [4717] 0.240 0.208 0.276 0.269 0.248 0.294 0.300 0.252 0.259 0.260 0.256 0.270
## [4729] 0.277 0.246 0.253 0.235 0.283 0.301 0.291 0.235 0.411 0.356 0.270 0.273
## [4741] 0.242 0.325 0.319 0.330 0.333 0.311 0.338 0.339 0.374 0.314 0.358 0.272
## [4753] 0.333 0.282 0.299 0.304 0.286 0.282 0.285 0.282 0.239 0.273 0.280 0.273
## [4765] 0.317 0.270 0.289 0.235 0.342 0.326 0.240 0.308 0.266 0.182 0.330 0.255
## [4777] 0.202 0.174 0.232 0.180 0.181 0.276 0.192 0.336 0.248 0.368 0.336 0.249
## [4789] 0.170 0.207 0.216 0.318 0.165 0.282 0.212 0.268 0.311 0.233 0.215 0.205
## [4801] 0.200 0.248 0.259 0.211 0.240 0.208 0.228 0.201 0.209 0.198 0.198 0.238
## [4813] 0.265 0.373 0.333 0.269 0.255 0.327 0.188 0.244 0.204 0.355 0.303 0.266
## [4825] 0.344 0.253 0.330 0.279 0.330 0.299 0.325 0.266 0.310 0.251 0.275 0.396
## [4837] 0.402 0.375 0.372 0.340 0.307 0.341 0.378 0.274 0.398 0.262 0.382 0.370
## [4849] 0.313 0.298 0.344 0.328 0.338 0.227 0.227 0.355 0.416 0.420 0.438 0.449
## [4861] 0.415 0.395 0.297 0.396 0.217 0.289 0.251 0.228 0.219 0.228 0.229 0.259
## [4873] 0.248 0.215 0.246 0.237 0.178 0.165 0.165 0.164 0.166 0.169 0.169 0.170
## [4885] 0.168 0.168 0.173 0.179 0.185 0.183 0.172 0.170 0.181 0.185 0.165 0.169
## [4897] 0.166 0.176 0.174 0.166 0.168 0.164 0.163 0.163 0.203 0.179 0.176 0.170
## [4909] 0.186 0.177 0.178 0.172 0.169 0.181 0.170 0.177 0.165 0.186 0.193 0.240
## [4921] 0.214 0.248 0.220 0.231 0.253 0.225 0.238 0.234 0.249 0.246 0.249 0.219
## [4933] 0.225 0.252 0.236 0.228 0.234 0.219 0.258 0.252 0.187 0.234 0.214 0.206
## [4945] 0.220 0.253 0.201 0.246 0.257 0.297 0.277 0.221 0.204 0.242 0.166 0.227
## [4957] 0.240 0.241 0.244 0.235 0.252 0.254 0.230 0.251 0.188 0.249 0.254 0.223
## [4969] 0.258 0.251 0.244 0.247 0.173 0.195 0.225 0.234 0.248 0.254 0.271 0.246
## [4981] 0.256 0.277 0.285 0.246 0.177 0.206 0.258 0.257 0.265 0.256 0.242 0.250
## [4993] 0.228 0.253 0.212 0.228 0.238 0.228 0.247 0.255 0.209 0.245 0.360 0.245
## [5005] 0.174 0.238 0.237 0.300 0.174 0.169 0.168 0.166 0.413 0.169 0.164 0.173
## [5017] 0.167 0.167 0.166 0.167 0.171 0.170 0.167 0.168 0.167 0.164 0.171 0.172
## [5029] 0.238 0.164 0.163 0.163 0.164 0.176 0.176 0.203 0.164 0.164 0.164 0.164
## [5041] 0.164 0.164 0.164 0.165 0.165 0.164 0.164 0.164 0.164 0.165 0.178 0.164
## [5053] 0.164 0.164 0.164 0.164 0.166 0.164 0.164 0.164 0.164 0.164 0.164 0.177
## [5065] 0.164 0.164 0.171 0.168 0.176 0.174 0.185 0.164 0.163 0.163 0.163 0.167
## [5077] 0.165 0.178 0.164 0.164 0.165 0.164 0.165 0.164 0.165 0.166 0.176 0.167
## [5089] 0.177 0.247 0.167 0.178 0.183 0.178 0.164 0.182 0.172 0.166 0.164 0.164
## [5101] 0.164 0.164 0.164 0.164 0.164 0.181 0.170 0.171 0.170 0.179 0.174 0.178
## [5113] 0.173 0.216 0.163 0.163 0.163 0.197 0.221 0.177 0.185 0.174 0.164 0.172
## [5125] 0.164 0.164 0.166 0.166 0.167 0.170 0.168 0.164 0.180 0.177 0.187 0.164
## [5137] 0.273 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.188 0.177
## [5149] 0.172 0.177 0.172 0.183 0.178 0.250 0.241 0.163 0.250 0.289 0.210 0.350
## [5161] 0.206 0.169 0.166 0.167 0.167 0.166 0.178 0.165 0.167 0.167 0.166 0.168
## [5173] 0.170 0.164 0.167 0.165 0.164 0.186 0.169 0.165 0.164 0.165 0.173 0.171
## [5185] 0.168 0.174 0.170 0.166 0.166 0.163 0.165 0.165 0.191 0.166 0.169 0.164
## [5197] 0.164 0.188 0.165 0.173 0.167 0.172 0.168 0.165 0.166 0.168 0.168 0.166
## [5209] 0.165 0.165 0.166 0.167 0.166 0.166 0.164 0.168 0.170 0.163 0.166 0.187
## [5221] 0.181 0.164 0.163 0.163 0.163 0.173 0.165 0.165 0.165 0.164 0.167 0.164
## [5233] 0.164 0.167 0.182 0.166 0.178 0.175 0.173 0.173 0.179 0.176 0.172 0.170
## [5245] 0.174 0.170 0.170 0.175 0.176 0.176 0.176 0.177 0.178 0.179 0.176 0.176
## [5257] 0.168 0.184 0.174 0.171 0.172 0.170 0.166 0.163 0.163 0.163 0.181 0.172
## [5269] 0.167 0.175 0.169 0.167 0.169 0.169 0.165 0.178 0.168 0.166 0.166 0.170
## [5281] 0.183 0.328 0.178 0.167 0.166 0.164 0.164 0.164 0.164 0.167 0.164 0.164
## [5293] 0.164 0.164 0.206 0.164 0.182 0.193 0.205 0.180 0.164 0.164 0.179 0.164
## [5305] 0.164 0.164 0.164 0.164 0.164 0.169 0.172 0.171 0.175 0.164 0.168 0.176
## [5317] 0.264 0.164 0.163 0.184 0.227 0.182 0.169 0.164 0.165 0.171 0.167 0.165
## [5329] 0.170 0.170 0.167 0.169 0.173 0.169 0.179 0.186 0.186 0.195 0.176 0.192
## [5341] 0.189 0.210 0.179 0.169 0.174 0.173 0.175 0.180 0.178 0.182 0.170 0.180
## [5353] 0.183 0.185 0.171 0.191 0.188 0.186 0.196 0.190 0.191 0.183 0.176 0.180
## [5365] 0.193 0.201 0.298 0.350 0.178 0.169 0.165 0.166 0.165 0.165 0.166 0.166
## [5377] 0.164 0.164 0.164 0.164 0.164 0.165 0.238 0.222 0.240 0.232 0.212 0.216
## [5389] 0.233 0.225 0.190 0.165 0.167 0.169 0.164 0.164 0.170 0.179 0.178 0.164
## [5401] 0.191 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.258 0.192 0.189 0.174
## [5413] 0.250 0.186 0.223 0.165 0.241 0.247 0.165 0.165 0.164 0.165 0.165 0.165
## [5425] 0.165 0.165 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [5437] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [5449] 0.298 0.243 0.163 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.162
## [5461] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [5473] 0.164 0.172 0.174 0.167 0.164 0.172 0.164 0.164 0.164 0.164 0.164 0.195
## [5485] 0.246 0.280 0.167 0.168 0.165 0.165 0.181 0.165 0.235 0.367 0.187 0.180
## [5497] 0.181 0.164 0.163 0.163 0.163 0.163 0.164 0.330 0.179 0.181 0.168 0.165
## [5509] 0.164 0.166 0.164 0.164 0.164 0.164 0.164 0.164 0.169 0.166 0.266 0.164
## [5521] 0.196 0.279 0.164 0.329 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164
## [5533] 0.202 0.213 0.191 0.192 0.220 0.177 0.217 0.169 0.163 0.173 0.188 0.164
## [5545] 0.164 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.164 0.164
## [5557] 0.361 0.162 0.162 0.165 0.165 0.435 0.216 0.204 0.204 0.331 0.163 0.165
## [5569] 0.163 0.163 0.163 0.163 0.166 0.166 0.192 0.164 0.176 0.165 0.165 0.163
## [5581] 0.163 0.164 0.164 0.164 0.165 0.165 0.264 0.233 0.211 0.249 0.238 0.438
## [5593] 0.363 0.318 0.316 0.295 0.226 0.311 0.327 0.439 0.356 0.197 0.181 0.194
## [5605] 0.164 0.164 0.165 0.165 0.164 0.302 0.229 0.273 0.262 0.368 0.163 0.163
## [5617] 0.163 0.163 0.163 0.163 0.163 0.164 0.164 0.179 0.164 0.163 0.164 0.164
## [5629] 0.165 0.164 0.164 0.179 0.190 0.221 0.265 0.270 0.295 0.256 0.267 0.289
## [5641] 0.264 0.215 0.277 0.262 0.305 0.312 0.284 0.289 0.205 0.300 0.234 0.193
## [5653] 0.189 0.197 0.659 0.322 0.232 0.221 0.167 0.318 0.253 0.509 0.214 0.163
## [5665] 0.163 0.163 0.163 0.163 0.163 0.163 0.222 0.255 0.303 0.164 0.164 0.166
## [5677] 0.167 0.170 0.164 0.166 0.165 0.169 0.173 0.167 0.170 0.167 0.168 0.175
## [5689] 0.180 0.164 0.253 0.292 0.220 0.303 0.295 0.209 0.170 0.170 0.227 0.199
## [5701] 0.259 0.311 0.257 0.179 0.183 0.164 0.169 0.170 0.192 0.255 0.368 0.347
## [5713] 0.257 0.238 0.283 0.231 0.212 0.222 0.229 0.310 0.276 0.229 0.274 0.278
## [5725] 0.311 0.325 0.262 0.272 0.229 0.277 0.205 0.314 0.307 0.311 0.288 0.343
## [5737] 0.330 0.294 0.618 0.310 0.374 0.320 0.215 0.164 0.196 0.178 0.171 0.165
## [5749] 0.212 0.195 0.243 0.264 0.270 0.167 0.176 0.230 0.168 0.184 0.165 0.167
## [5761] 0.186 0.170 0.165 0.165 0.169 0.179 0.176 0.170 0.180 0.162 0.209 0.164
## [5773] 0.210 0.164 0.164 0.164 0.194 0.194 0.231 0.236 0.225 0.307 0.201 0.179
## [5785] 0.166 0.191 0.210 0.241 0.179 0.202 0.239 0.166 0.166 0.166 0.165 0.165
## [5797] 0.166 0.172 0.176 0.165 0.165 0.165 0.164 0.162 0.164 0.165 0.165 0.163
## [5809] 0.163 0.170 0.164 0.175 0.165 0.165 0.164 0.165 0.165 0.164 0.165 0.164
## [5821] 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.165 0.165 0.165 0.164 0.165
## [5833] 0.164 0.165 0.165 0.165 0.165 0.164 0.164 0.165 0.170 0.164 0.165 0.165
## [5845] 0.163 0.255 0.309 0.366 0.165 0.165 0.164 0.164 0.165 0.165 0.165 0.165
## [5857] 0.165 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.189 0.209 0.165 0.175
## [5869] 0.164 0.164 0.165 0.173 0.164 0.336 0.282 0.275 0.244 0.308 0.575 0.164
## [5881] 0.165 0.165 0.164 0.164 0.165 0.168 0.164 0.164 0.168 0.168 0.165 0.165
## [5893] 0.190 0.165 0.164 0.196 0.165 0.176 0.164 0.165 0.277 0.208 0.165 0.303
## [5905] 0.165 0.164 0.253 0.300 0.298 0.296 0.299 0.175 0.214 0.232 0.217 0.199
## [5917] 0.179 0.213 0.207 0.177 0.199 0.237 0.169 0.170 0.169 0.189 0.240 0.169
## [5929] 0.166 0.237 0.165 0.165 0.164 0.164 0.165 0.319 0.308 0.247 0.235 0.263
## [5941] 0.236 0.240 0.229 0.309 0.323 0.292 0.215 0.357 0.165 0.165 0.347 0.371
## [5953] 0.165 0.205 0.353 0.259 0.287 0.187 0.165 0.165 0.163 0.163 0.164 0.164
## [5965] 0.165 0.165 0.248 0.369 0.355 0.164 0.165 0.165 0.165 0.164 0.165 0.165
## [5977] 0.165 0.164 0.164 0.165 0.164 0.165 0.165 0.164 0.321 0.295 0.344 0.164
## [5989] 0.163 0.165 0.164 0.164 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163
## [6001] 0.163 0.164 0.164 0.387 0.164 0.163 0.184 0.475 0.164 0.164 0.164 0.164
## [6013] 0.163 0.165 0.164 0.164 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163
## [6025] 0.163 0.164 0.168 0.328 0.274 0.259 0.292 0.286 0.352 0.191 0.164 0.164
## [6037] 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.203 0.202 0.181
## [6049] 0.164 0.164 0.164 0.164 0.164 0.313 0.340 0.488 0.498 0.343 0.164 0.164
## [6061] 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.176 0.164 0.234
## [6073] 0.164 0.222 0.164 0.165 0.210 0.280 0.284 0.238 0.222 0.257 0.260 0.321
## [6085] 0.292 0.195 0.229 0.176 0.195 0.234 0.331 0.219 0.349 0.249 0.168 0.173
## [6097] 0.203 0.223 0.203 0.231 0.220 0.294 0.209 0.164 0.166 0.165 0.164 0.168
## [6109] 0.165 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.165 0.165 0.165
## [6121] 0.165 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.199 0.203 0.172 0.165
## [6133] 0.165 0.163 0.164 0.164 0.164 0.165 0.165 0.196 0.192 0.284 0.200 0.172
## [6145] 0.167 0.171 0.265 0.283 0.202 0.164 0.164 0.164 0.237 0.164 0.171 0.183
## [6157] 0.222 0.219 0.188 0.165 0.184 0.164 0.164 0.165 0.165 0.164 0.180 0.194
## [6169] 0.277 0.298 0.254 0.282 0.233 0.193 0.199 0.325 0.277 0.209 0.223 0.165
## [6181] 0.227 0.183 0.267 0.195 0.331 0.187 0.164 0.171 0.169 0.205 0.183 0.210
## [6193] 0.180 0.345 0.184 0.177 0.185 0.199 0.204 0.197 0.251 0.232 0.248 0.239
## [6205] 0.266 0.276 0.238 0.220 0.283 0.258 0.282 0.281 0.304 0.291 0.243 0.266
## [6217] 0.276 0.263 0.262 0.232 0.214 0.314 0.180 0.165 0.365 0.164 0.164 0.164
## [6229] 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.164 0.164 0.196 0.164
## [6241] 0.198 0.265 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.164
## [6253] 0.181 0.163 0.164 0.163 0.204 0.207 0.337 0.219 0.248 0.267 0.244 0.304
## [6265] 0.302 0.324 0.287 0.282 0.285 0.290 0.284 0.330 0.357 0.337 0.244 0.196
## [6277] 0.361 0.216 0.257 0.271 0.298 0.323 0.254 0.264 0.191 0.165 0.227 0.175
## [6289] 0.183 0.165 0.195 0.229 0.201 0.258 0.268 0.564 0.162 0.165 0.164 0.411
## [6301] 0.223 0.228 0.169 0.167 0.169 0.165 0.193 0.168 0.165 0.175 0.179 0.167
## [6313] 0.165 0.211 0.229 0.168 0.165 0.165 0.165 0.166 0.165 0.165 0.165 0.165
## [6325] 0.173 0.164 0.199 0.162 0.165 0.164 0.164 0.164 0.236 0.197 0.267 0.183
## [6337] 0.186 0.199 0.254 0.223 0.187 0.182 0.202 0.214 0.173 0.201 0.167 0.198
## [6349] 0.162 0.227 0.164 0.236 0.189 0.168 0.183 0.211 0.228 0.164 0.164 0.164
## [6361] 0.163 0.163 0.187 0.306 0.214 0.257 0.203 0.197 0.179 0.177 0.530 0.269
## [6373] 0.303 0.257 0.249 0.258 0.170 0.167 0.243 0.189 0.237 0.320 0.243 0.245
## [6385] 0.249 0.250 0.234 0.264 0.265 0.164 0.166 0.167 0.453 0.185 0.255 0.211
## [6397] 0.177 0.168 0.186 0.176 0.180 0.234 0.212 0.251 0.267 0.237 0.210 0.214
## [6409] 0.250 0.294 0.247 0.175 0.198 0.214 0.222 0.304 0.220 0.268 0.276 0.306
## [6421] 0.230 0.272 0.220 0.223 0.302 0.320 0.286 0.261 0.164 0.255 0.164 0.163
## [6433] 0.164 0.164 0.165 0.164 0.253 0.181 0.195 0.244 0.244 0.250 0.256 0.244
## [6445] 0.279 0.294 0.314 0.289 0.279 0.246 0.279 0.297 0.206 0.206 0.360 0.163
## [6457] 0.181 0.189 0.189 0.218 0.215 0.196 0.218 0.170 0.484 0.370 0.276 0.249
## [6469] 0.310 0.309 0.229 0.275 0.214 0.247 0.300 0.164 0.312 0.305 0.316 0.212
## [6481] 0.178 0.187 0.246 0.187 0.173 0.175 0.207 0.241 0.179 0.189 0.194 0.230
## [6493] 0.689 0.214 0.165 0.368 0.213 0.419 0.274 0.176 0.184 0.193 0.164 0.165
## [6505] 0.174 0.173 0.177 0.171 0.192 0.177 0.379 0.352 0.180 0.174 0.324 0.184
## [6517] 0.164 0.165 0.168 0.164 0.164 0.165 0.165 0.162 0.164 0.170 0.163 0.164
## [6529] 0.164 0.165 0.164 0.164 0.164 0.165 0.172 0.182 0.184 0.230 0.274 0.639
## [6541] 0.292 0.182 0.192 0.624 0.164 0.166 0.162 0.180 0.199 0.165 0.164 0.162
## [6553] 0.164 0.178 0.164 0.289 0.245 0.235 0.202 0.196 0.172 0.162 0.169 0.176
## [6565] 0.170 0.174 0.200 0.162 0.253 0.262 0.171 0.232 0.262 0.227 0.220 0.221
## [6577] 0.190 0.215 0.262 0.226 0.311 0.283 0.221 0.229 0.296 0.405 0.262 0.163
## [6589] 0.163 0.163 0.163 0.163 0.164 0.164 0.163 0.164 0.163 0.163 0.163 0.163
## [6601] 0.163 0.184 0.164 0.232 0.164 0.163 0.163 0.163 0.163 0.167 0.223 0.163
## [6613] 0.263 0.166 0.164 0.268 0.302 0.232 0.304 0.238 0.165 0.180 0.352 0.288
## [6625] 0.273 0.205 0.165 0.165 0.165 0.164 0.165 0.165 0.165 0.172 0.165 0.222
## [6637] 0.169 0.180 0.165 0.167 0.164 0.164 0.165 0.165 0.164 0.165 0.165 0.165
## [6649] 0.165 0.165 0.165 0.165 0.165 0.162 0.165 0.182 0.165 0.164 0.165 0.165
## [6661] 0.165 0.165 0.165 0.165 0.165 0.162 0.337 0.165 0.167 0.165 0.204 0.340
## [6673] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.162 0.165 0.165 0.165
## [6685] 0.165 0.162 0.168 0.166 0.164 0.169 0.193 0.176 0.185 0.168 0.211 0.194
## [6697] 0.164 0.165 0.204 0.237 0.197 0.302 0.178 0.162 0.172 0.173 0.168 0.162
## [6709] 0.171 0.162 0.172 0.164 0.164 0.162 0.162 0.174 0.164 0.164 0.164 0.164
## [6721] 0.164 0.171 0.164 0.164 0.164 0.168 0.164 0.328 0.333 0.164 0.164 0.164
## [6733] 0.263 0.165 0.292 0.166 0.165 0.164 0.162 0.163 0.163 0.164 0.164 0.162
## [6745] 0.163 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.164 0.162 0.162 0.163
## [6757] 0.163 0.163 0.162 0.162 0.163 0.165 0.164 0.162 0.162 0.163 0.163 0.164
## [6769] 0.162 0.165 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.163 0.164 0.162
## [6781] 0.162 0.163 0.163 0.163 0.162 0.165 0.169 0.163 0.163 0.164 0.162 0.166
## [6793] 0.163 0.163 0.164 0.162 0.173 0.163 0.163 0.164 0.162 0.162 0.163 0.163
## [6805] 0.164 0.162 0.162 0.163 0.165 0.169 0.162 0.162 0.163 0.185 0.164 0.162
## [6817] 0.165 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.164 0.162 0.162 0.163
## [6829] 0.165 0.163 0.162 0.162 0.163 0.163 0.164 0.162 0.163 0.163 0.164 0.162
## [6841] 0.163 0.165 0.164 0.162 0.163 0.163 0.164 0.162 0.163 0.165 0.164 0.162
## [6853] 0.163 0.163 0.164 0.162 0.163 0.163 0.164 0.162 0.163 0.163 0.164 0.162
## [6865] 0.163 0.164 0.162 0.162 0.163 0.163 0.162 0.164 0.162 0.163 0.163 0.162
## [6877] 0.163 0.162 0.162 0.163 0.162 0.162 0.163 0.162 0.162 0.163 0.164 0.162
## [6889] 0.164 0.162 0.163 0.165 0.162 0.164 0.165 0.163 0.166 0.162 0.164 0.162
## [6901] 0.163 0.165 0.162 0.164 0.162 0.163 0.164 0.162 0.164 0.162 0.163 0.163
## [6913] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
## [6925] 0.163 0.164 0.164 0.162 0.163 0.163 0.164 0.164 0.164 0.162 0.163 0.163
## [6937] 0.164 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
## [6949] 0.163 0.164 0.164 0.164 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
## [6961] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
## [6973] 0.163 0.164 0.164 0.162 0.163 0.163 0.165 0.164 0.164 0.162 0.163 0.163
## [6985] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.165 0.163 0.163
## [6997] 0.164 0.165 0.164 0.162 0.163 0.163 0.165 0.164 0.162 0.163 0.163 0.165
## [7009] 0.163 0.162 0.163 0.163 0.165 0.163 0.162 0.163 0.163 0.163 0.164 0.162
## [7021] 0.163 0.165 0.165 0.162 0.162 0.163 0.163 0.165 0.164 0.165 0.163 0.163
## [7033] 0.164 0.164 0.162 0.163 0.163 0.164 0.163 0.162 0.163 0.163 0.162 0.163
## [7045] 0.163 0.163 0.163 0.164 0.162 0.163 0.163 0.163 0.165 0.163 0.163 0.163
## [7057] 0.163 0.164 0.162 0.162 0.163 0.163 0.164 0.162 0.162 0.163 0.164 0.164
## [7069] 0.162 0.162 0.163 0.165 0.164 0.163 0.165 0.163 0.163 0.164 0.162 0.162
## [7081] 0.163 0.163 0.164 0.162 0.162 0.163 0.165 0.164 0.162 0.162 0.163 0.163
## [7093] 0.164 0.162 0.163 0.163 0.164 0.162 0.163 0.165 0.164 0.162 0.163 0.163
## [7105] 0.164 0.162 0.163 0.165 0.164 0.174 0.163 0.163 0.164 0.171 0.182 0.163
## [7117] 0.163 0.172 0.209 0.247 0.213 0.169 0.168 0.179 0.314 0.166 0.172 0.262
## [7129] 0.178 0.177 0.242 0.174 0.273 0.175 0.213 0.174 0.201 0.168 0.271 0.167
## [7141] 0.305 0.169 0.209 0.167 0.163 0.194 0.165 0.163 0.162 0.222 0.162 0.163
## [7153] 0.167 0.163 0.165 0.162 0.165 0.162 0.164 0.163 0.164 0.162 0.164 0.162
## [7165] 0.165 0.162 0.164 0.162 0.164 0.162 0.164 0.162 0.163 0.163 0.164 0.164
## [7177] 0.162 0.163 0.163 0.164 0.164 0.162 0.163 0.163 0.164 0.164 0.162 0.163
## [7189] 0.164 0.164 0.164 0.162 0.163 0.164 0.164 0.164 0.162 0.163 0.163 0.164
## [7201] 0.164 0.162 0.163 0.163 0.164 0.164 0.164 0.163 0.163 0.164 0.163 0.162
## [7213] 0.163 0.163 0.164 0.164 0.162 0.163 0.163 0.165 0.164 0.162 0.163 0.163
## [7225] 0.165 0.164 0.162 0.163 0.165 0.164 0.164 0.162 0.163 0.163 0.165 0.164
## [7237] 0.162 0.163 0.163 0.164 0.172 0.165 0.163 0.165 0.165 0.164 0.162 0.163
## [7249] 0.163 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.162 0.162 0.163
## [7261] 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.163 0.163 0.162 0.162 0.163
## [7273] 0.163 0.165 0.164 0.162 0.162 0.163 0.163 0.163 0.164 0.162 0.165 0.163
## [7285] 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.163 0.164 0.162 0.162 0.163
## [7297] 0.163 0.163 0.162 0.163 0.163 0.163 0.163 0.163 0.164 0.162 0.163 0.163
## [7309] 0.163 0.163 0.164 0.162 0.163 0.163 0.163 0.163 0.164 0.162 0.162 0.163
## [7321] 0.163 0.166 0.164 0.162 0.162 0.163 0.163 0.164 0.164 0.162 0.162 0.163
## [7333] 0.163 0.167 0.164 0.162 0.165 0.163 0.163 0.163 0.164 0.162 0.162 0.163
## [7345] 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.165 0.165 0.162 0.162 0.163
## [7357] 0.163 0.163 0.164 0.162 0.163 0.163 0.163 0.164 0.162 0.163 0.163 0.165
## [7369] 0.164 0.162 0.163 0.163 0.164 0.164 0.162 0.163 0.165 0.164 0.162 0.163
## [7381] 0.163 0.164 0.162 0.163 0.163 0.164 0.162 0.163 0.163 0.164 0.162 0.163
## [7393] 0.164 0.165 0.162 0.163 0.163 0.162 0.164 0.162 0.163 0.163 0.162 0.163
## [7405] 0.162 0.164 0.162 0.163 0.162 0.162 0.163 0.162 0.162 0.163 0.164 0.162
## [7417] 0.164 0.162 0.163 0.165 0.162 0.164 0.163 0.163 0.164 0.162 0.164 0.162
## [7429] 0.163 0.165 0.162 0.164 0.162 0.163 0.164 0.162 0.164 0.162 0.163 0.163
## [7441] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
## [7453] 0.163 0.164 0.164 0.162 0.163 0.163 0.164 0.164 0.164 0.162 0.163 0.163
## [7465] 0.164 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
## [7477] 0.163 0.164 0.164 0.164 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
## [7489] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
## [7501] 0.163 0.166 0.164 0.162 0.163 0.163 0.165 0.164 0.164 0.162 0.163 0.163
## [7513] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.165 0.165 0.163 0.163
## [7525] 0.164 0.163 0.186 0.163 0.163 0.167 0.163 0.193 0.163 0.165 0.187 0.181
## [7537] 0.180 0.163 0.163 0.177 0.162 0.164 0.163 0.163 0.163 0.170 0.163 0.163
## [7549] 0.163 0.208 0.174 0.166 0.163 0.189 0.173 0.167 0.175 0.163 0.163 0.169
## [7561] 0.174 0.170 0.163 0.163 0.167 0.182 0.166 0.163 0.163 0.170 0.163 0.171
## [7573] 0.163 0.163 0.195 0.169 0.163 0.163 0.163 0.163 0.171 0.163 0.163 0.173
## [7585] 0.163 0.167 0.162 0.163 0.163 0.166 0.164 0.162 0.163 0.163 0.164 0.162
## [7597] 0.164 0.163 0.163 0.189 0.172 0.168 0.163 0.163 0.165 0.181 0.162 0.163
## [7609] 0.163 0.166 0.164 0.169 0.162 0.163 0.163 0.165 0.163 0.171 0.163 0.163
## [7621] 0.192 0.167 0.168 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.165 0.185
## [7633] 0.163 0.163 0.163 0.180 0.163 0.165 0.230 0.163 0.176 0.164 0.243 0.164
## [7645] 0.163 0.165 0.267 0.164 0.235 0.228 0.173 0.165 0.193 0.164 0.233 0.187
## [7657] 0.163 0.165 0.268 0.176 0.260 0.181 0.163 0.165 0.260 0.164 0.217 0.177
## [7669] 0.165 0.246 0.164 0.238 0.167 0.186 0.204 0.164 0.232 0.170 0.178 0.238
## [7681] 0.164 0.196 0.177 0.197 0.211 0.164 0.206 0.185 0.173 0.178 0.164 0.175
## [7693] 0.176 0.248 0.162 0.164 0.181 0.169 0.167 0.162 0.164 0.163 0.167 0.163
## [7705] 0.162 0.171 0.165 0.163 0.162 0.163 0.168 0.165 0.163 0.162 0.163 0.169
## [7717] 0.163 0.163 0.200 0.163 0.162 0.163 0.163 0.226 0.163 0.163 0.163 0.163
## [7729] 0.184 0.163 0.170 0.163 0.163 0.184 0.163 0.165 0.163 0.163 0.187 0.163
## [7741] 0.165 0.163 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.163 0.164 0.163
## [7753] 0.163 0.163 0.163 0.162 0.163 0.163 0.168 0.164 0.162 0.163 0.163 0.176
## [7765] 0.163 0.162 0.163 0.163 0.176 0.163 0.170 0.163 0.173 0.180 0.163 0.231
## [7777] 0.163 0.164 0.170 0.163 0.182 0.163 0.163 0.173 0.163 0.190 0.163 0.163
## [7789] 0.163 0.165 0.163 0.239 0.253 0.251 0.172 0.163 0.255 0.254 0.242 0.174
## [7801] 0.164 0.224 0.259 0.239 0.251 0.225 0.180 0.253 0.211 0.207 0.275 0.163
## [7813] 0.197 0.252 0.203 0.230 0.237 0.212 0.260 0.211 0.242 0.268 0.164 0.223
## [7825] 0.248 0.256 0.236 0.250 0.172 0.262 0.241 0.234 0.263 0.164 0.162 0.237
## [7837] 0.238 0.220 0.228 0.174 0.166 0.237 0.208 0.215 0.277 0.164 0.199 0.244
## [7849] 0.222 0.259 0.275 0.163 0.244 0.247 0.215 0.185 0.162 0.182 0.248 0.253
## [7861] 0.173 0.164 0.220 0.215 0.255 0.175 0.175 0.214 0.220 0.244 0.176 0.162
## [7873] 0.203 0.197 0.199 0.174 0.167 0.217 0.185 0.199 0.169 0.168 0.263 0.205
## [7885] 0.243 0.182 0.163 0.253 0.259 0.253 0.181 0.162 0.164 0.239 0.238 0.179
## [7897] 0.164 0.164 0.164 0.250 0.258 0.173 0.163 0.164 0.252 0.278 0.171 0.162
## [7909] 0.164 0.235 0.165 0.204 0.164 0.166 0.164 0.237 0.165 0.180 0.164 0.187
## [7921] 0.408 0.246 0.165 0.205 0.164 0.191 0.309 0.232 0.165 0.185 0.164 0.201
## [7933] 0.217 0.236 0.165 0.269 0.164 0.207 0.220 0.220 0.165 0.212 0.164 0.199
## [7945] 0.234 0.165 0.214 0.164 0.215 0.234 0.210 0.180 0.164 0.205 0.225 0.247
## [7957] 0.216 0.164 0.233 0.244 0.225 0.187 0.164 0.220 0.238 0.239 0.241 0.164
## [7969] 0.226 0.256 0.223 0.184 0.164 0.166 0.242 0.237 0.178 0.181 0.240 0.235
## [7981] 0.196 0.187 0.165 0.210 0.182 0.163 0.194 0.252 0.237 0.167 0.189 0.163
## [7993] 0.180 0.258 0.224 0.251 0.163 0.208 0.258 0.232 0.275 0.163 0.209 0.247
## [8005] 0.212 0.270 0.164 0.163 0.186 0.251 0.243 0.267 0.164 0.163 0.252 0.253
## [8017] 0.221 0.261 0.164 0.163 0.246 0.257 0.228 0.249 0.163 0.254 0.241 0.186
## [8029] 0.271 0.164 0.163 0.170 0.221 0.225 0.237 0.164 0.163 0.188 0.260 0.219
## [8041] 0.243 0.164 0.201 0.244 0.253 0.194 0.163 0.173 0.244 0.248 0.245 0.164
## [8053] 0.163 0.174 0.252 0.261 0.272 0.164 0.163 0.232 0.240 0.239 0.243 0.163
## [8065] 0.252 0.236 0.237 0.212 0.163 0.238 0.242 0.223 0.163 0.178 0.177 0.172
## [8077] 0.163 0.181 0.186 0.163 0.176 0.179 0.163 0.163 0.177 0.165 0.178 0.177
## [8089] 0.163 0.181 0.178 0.163 0.185 0.174 0.163 0.181 0.177 0.163 0.180 0.180
## [8101] 0.179 0.178 0.163 0.176 0.179 0.163 0.177 0.178 0.163 0.184 0.182 0.164
## [8113] 0.187 0.176 0.165 0.185 0.179 0.163 0.182 0.182 0.163 0.178 0.178 0.165
## [8125] 0.181 0.182 0.163 0.180 0.177 0.163 0.178 0.179 0.165 0.182 0.177 0.163
## [8137] 0.174 0.176 0.165 0.181 0.178 0.163 0.176 0.175 0.163 0.175 0.177 0.163
## [8149] 0.173 0.175 0.164 0.177 0.181 0.163 0.164 0.174 0.164 0.165 0.179 0.163
## [8161] 0.164 0.178 0.192 0.164 0.184 0.164 0.178 0.164 0.166 0.176 0.188 0.174
## [8173] 0.168 0.174 0.178 0.173 0.173 0.177 0.174 0.173 0.175 0.175 0.171 0.177
## [8185] 0.174 0.181 0.176 0.179 0.174 0.174 0.166 0.182 0.182 0.175 0.186 0.171
## [8197] 0.173 0.173 0.174 0.174 0.169 0.175 0.176 0.178 0.187 0.178 0.176 0.165
## [8209] 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.163 0.165 0.164 0.163 0.164
## [8221] 0.163 0.164 0.163 0.164 0.163 0.162 0.163 0.164 0.163 0.165 0.163 0.164
## [8233] 0.163 0.164 0.164 0.164 0.165 0.164 0.163 0.164 0.163 0.164 0.165 0.164
## [8245] 0.163 0.164 0.163 0.164 0.165 0.164 0.163 0.164 0.165 0.164 0.163 0.164
## [8257] 0.163 0.164 0.163 0.164 0.164 0.169 0.163 0.164 0.164 0.194 0.163 0.162
## [8269] 0.162 0.162 0.164 0.163 0.165 0.162 0.164 0.162 0.165 0.162 0.164 0.162
## [8281] 0.163 0.164 0.163 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.163 0.164
## [8293] 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.165 0.163 0.164 0.165 0.165
## [8305] 0.163 0.164 0.163 0.164 0.165 0.167 0.165 0.163 0.163 0.169 0.177 0.179
## [8317] 0.163 0.163 0.167 0.171 0.174 0.163 0.163 0.180 0.179 0.164 0.163 0.163
## [8329] 0.163 0.186 0.169 0.163 0.165 0.167 0.185 0.164 0.163 0.163 0.165 0.181
## [8341] 0.165 0.163 0.163 0.172 0.176 0.187 0.163 0.163 0.166 0.177 0.189 0.163
## [8353] 0.163 0.173 0.178 0.183 0.163 0.163 0.175 0.183 0.185 0.163 0.163 0.181
## [8365] 0.190 0.170 0.163 0.163 0.178 0.185 0.189 0.163 0.163 0.185 0.185 0.190
## [8377] 0.163 0.164 0.189 0.166 0.185 0.163 0.165 0.187 0.180 0.182 0.163 0.188
## [8389] 0.186 0.188 0.163 0.176 0.187 0.182 0.165 0.179 0.180 0.180 0.163 0.178
## [8401] 0.177 0.164 0.163 0.178 0.180 0.164 0.165 0.179 0.186 0.164 0.163 0.176
## [8413] 0.187 0.164 0.165 0.183 0.197 0.164 0.163 0.178 0.182 0.162 0.163 0.183
## [8425] 0.200 0.188 0.163 0.326 0.215 0.182 0.253 0.321 0.304 0.250 0.238 0.251
## [8437] 0.333 0.212 0.205 0.297 0.267 0.183 0.248 0.292 0.217 0.319 0.274 0.210
## [8449] 0.256 0.314 0.228 0.169 0.262 0.189 0.164 0.187 0.164 0.182 0.189 0.187
## [8461] 0.165 0.184 0.179 0.188 0.164 0.187 0.181 0.184 0.165 0.184 0.181 0.188
## [8473] 0.164 0.189 0.181 0.191 0.163 0.163 0.187 0.183 0.178 0.163 0.163 0.190
## [8485] 0.176 0.183 0.163 0.163 0.188 0.177 0.191 0.163 0.164 0.186 0.182 0.192
## [8497] 0.163 0.164 0.186 0.181 0.189 0.163 0.163 0.186 0.184 0.191 0.163 0.163
## [8509] 0.185 0.183 0.164 0.163 0.163 0.189 0.187 0.164 0.163 0.163 0.179 0.183
## [8521] 0.174 0.163 0.163 0.177 0.175 0.173 0.163 0.163 0.182 0.173 0.187 0.163
## [8533] 0.165 0.178 0.182 0.188 0.163 0.163 0.172 0.168 0.177 0.163 0.163 0.169
## [8545] 0.178 0.165 0.163 0.168 0.165 0.163 0.163 0.163 0.163 0.165 0.163 0.163
## [8557] 0.163 0.163 0.163 0.163 0.163 0.163 0.164 0.165 0.163 0.163 0.257 0.164
## [8569] 0.165 0.164 0.165 0.164 0.163 0.163 0.163 0.164 0.164 0.163 0.163 0.163
## [8581] 0.163 0.163 0.163 0.165 0.163 0.163 0.164 0.164 0.164 0.164 0.164 0.164
## [8593] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8605] 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8617] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8629] 0.163 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8641] 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8653] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8665] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8677] 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164
## [8689] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8701] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8713] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8725] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8737] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [8749] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164
## [8761] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.163 0.164 0.164 0.164
## [8773] 0.164 0.164 0.164 0.163 0.164 0.164 0.287 0.358 0.164 0.268 0.164 0.164
## [8785] 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.176 0.172 0.170 0.167 0.165
## [8797] 0.166 0.167 0.165 0.167 0.168 0.166 0.167 0.166 0.166 0.167 0.167 0.168
## [8809] 0.169 0.168 0.169 0.166 0.166 0.167 0.167 0.166 0.166 0.167 0.167 0.166
## [8821] 0.167 0.167 0.169 0.167 0.167 0.166 0.168 0.167 0.174 0.166 0.169 0.168
## [8833] 0.167 0.166 0.167 0.168 0.167 0.168 0.167 0.168 0.167 0.165 0.165 0.166
## [8845] 0.168 0.168 0.166 0.166 0.166 0.166 0.167 0.166 0.167 0.167 0.165 0.167
## [8857] 0.167 0.166 0.166 0.168 0.167 0.169 0.166 0.166 0.167 0.166 0.166 0.165
## [8869] 0.166 0.166 0.166 0.168 0.167 0.166 0.166 0.168 0.170 0.169 0.165 0.167
## [8881] 0.168 0.169 0.169 0.166 0.166 0.166 0.167 0.169 0.167 0.168 0.167 0.163
## [8893] 0.164 0.166 0.166 0.165 0.168 0.172 0.167 0.167 0.167 0.166 0.171 0.170
## [8905] 0.169 0.170 0.166 0.164 0.165 0.165 0.165 0.165 0.165 0.166 0.166 0.165
## [8917] 0.166 0.166 0.166 0.166 0.166 0.165 0.165 0.168 0.181 0.178 0.164 0.168
## [8929] 0.167 0.166 0.166 0.165 0.166 0.166 0.166 0.165 0.166 0.166 0.166 0.165
## [8941] 0.165 0.168 0.167 0.167 0.169 0.167 0.166 0.167 0.167 0.166 0.167 0.166
## [8953] 0.166 0.166 0.166 0.166 0.177 0.166 0.167 0.166 0.168 0.168 0.167 0.166
## [8965] 0.169 0.168 0.170 0.169 0.167 0.168 0.167 0.168 0.169 0.169 0.167 0.167
## [8977] 0.167 0.166 0.170 0.173 0.173 0.167 0.167 0.167 0.167 0.167 0.168 0.167
## [8989] 0.170 0.167 0.170 0.170 0.168 0.168 0.167 0.169 0.167 0.167 0.166 0.167
## [9001] 0.164 0.166 0.167 0.167 0.165 0.165 0.164 0.164 0.164 0.166 0.166 0.167
## [9013] 0.167 0.165 0.163 0.164 0.165 0.165 0.165 0.164 0.166 0.164 0.164 0.165
## [9025] 0.165 0.165 0.166 0.165 0.166 0.165 0.165 0.166 0.165 0.165 0.164 0.165
## [9037] 0.165 0.166 0.166 0.165 0.166 0.165 0.169 0.233 0.165 0.165 0.165 0.165
## [9049] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.166 0.166 0.166
## [9061] 0.165 0.165 0.166 0.165 0.166 0.166 0.165 0.166 0.165 0.163 0.165 0.165
## [9073] 0.165 0.166 0.165 0.166 0.166 0.166 0.166 0.165 0.166 0.166 0.166 0.165
## [9085] 0.166 0.166 0.166 0.165 0.164 0.164 0.166 0.166 0.165 0.166 0.166 0.165
## [9097] 0.165 0.165 0.165 0.165 0.166 0.165 0.166 0.166 0.165 0.165 0.163 0.165
## [9109] 0.165 0.165 0.165 0.164 0.165 0.165 0.166 0.165 0.165 0.166 0.166 0.165
## [9121] 0.165 0.165 0.166 0.165 0.165 0.165 0.169 0.166 0.169 0.165 0.167 0.168
## [9133] 0.167 0.165 0.166 0.165 0.165 0.165 0.167 0.166 0.169 0.175 0.178 0.174
## [9145] 0.168 0.173 0.176 0.175 0.176 0.177 0.167 0.167 0.165 0.165 0.167 0.166
## [9157] 0.167 0.167 0.167 0.167 0.166 0.165 0.165 0.165 0.166 0.165 0.165 0.165
## [9169] 0.166 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.166 0.165 0.165
## [9181] 0.165 0.165 0.166 0.166 0.166 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [9193] 0.165 0.165 0.165 0.167 0.165 0.165 0.165 0.165 0.164 0.165 0.166 0.165
## [9205] 0.165 0.165 0.165 0.165 0.163 0.165 0.165 0.164 0.165 0.164 0.165 0.165
## [9217] 0.165 0.165 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.165 0.164 0.164
## [9229] 0.166 0.167 0.165 0.164 0.168 0.171 0.168 0.167 0.174 0.168 0.176 0.182
## [9241] 0.173 0.169 0.170 0.181 0.172 0.169 0.169 0.168 0.164 0.168 0.165 0.168
## [9253] 0.166 0.166 0.168 0.168 0.166 0.167 0.171 0.169 0.167 0.166 0.171 0.165
## [9265] 0.165 0.165 0.174 0.165 0.170 0.164 0.169 0.165 0.167 0.170 0.166 0.169
## [9277] 0.170 0.172 0.168 0.177 0.182 0.175 0.176 0.175 0.168 0.165 0.168 0.165
## [9289] 0.166 0.166 0.167 0.165 0.164 0.169 0.166 0.167 0.167 0.165 0.165 0.165
## [9301] 0.165 0.165 0.164 0.165 0.165 0.165 0.165 0.177 0.165 0.182 0.167 0.178
## [9313] 0.175 0.179 0.175 0.167 0.167 0.182 0.165 0.165 0.165 0.165 0.165 0.167
## [9325] 0.165 0.165 0.164 0.166 0.170 0.166 0.166 0.168 0.166 0.166 0.167 0.167
## [9337] 0.166 0.171 0.165 0.164 0.166 0.164 0.168 0.168 0.173 0.171 0.169 0.166
## [9349] 0.166 0.167 0.170 0.165 0.171 0.171 0.166 0.165 0.165 0.166 0.167 0.167
## [9361] 0.166 0.170 0.166 0.167 0.164 0.167 0.170 0.166 0.167 0.172 0.164 0.165
## [9373] 0.174 0.172 0.166 0.166 0.174 0.174 0.165 0.166 0.166 0.165 0.167 0.164
## [9385] 0.167 0.164 0.168 0.167 0.166 0.169 0.166 0.168 0.165 0.168 0.171 0.167
## [9397] 0.166 0.166 0.166 0.165 0.167 0.167 0.164 0.167 0.168 0.165 0.166 0.169
## [9409] 0.165 0.167 0.170 0.165 0.165 0.165 0.165 0.167 0.164 0.166 0.165 0.165
## [9421] 0.165 0.164 0.165 0.165 0.164 0.180 0.180 0.217 0.231 0.238 0.228 0.247
## [9433] 0.289 0.228 0.179 0.165 0.165 0.165 0.164 0.164 0.165 0.164 0.165 0.165
## [9445] 0.165 0.165 0.165 0.167 0.166 0.168 0.166 0.166 0.166 0.166 0.164 0.164
## [9457] 0.164 0.162 0.162 0.162 0.162 0.162 0.164 0.164 0.214 0.203 0.198 0.210
## [9469] 0.173 0.165 0.186 0.178 0.165 0.175 0.167 0.167 0.173 0.174 0.170 0.169
## [9481] 0.165 0.166 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.164
## [9493] 0.164 0.164 0.165 0.164 0.164 0.168 0.166 0.167 0.166 0.166 0.165 0.165
## [9505] 0.165 0.164 0.166 0.165 0.166 0.165 0.167 0.166 0.166 0.166 0.164 0.223
## [9517] 0.204 0.195 0.183 0.205 0.200 0.178 0.167 0.168 0.166 0.169 0.172 0.168
## [9529] 0.168 0.168 0.168 0.168 0.171 0.176 0.171 0.190 0.183 0.181 0.186 0.196
## [9541] 0.190 0.191 0.184 0.183 0.184 0.185 0.186 0.185 0.184 0.185 0.179 0.176
## [9553] 0.183 0.178 0.172 0.178 0.172 0.179 0.176 0.174 0.174 0.170 0.176 0.171
## [9565] 0.170 0.169 0.174 0.171 0.173 0.172 0.170 0.168 0.167 0.169 0.179 0.164
## [9577] 0.168 0.180 0.165 0.168 0.165 0.174 0.169 0.171 0.165 0.165 0.175 0.165
## [9589] 0.182 0.178 0.168 0.163 0.182 0.164 0.164 0.181 0.185 0.177 0.178 0.181
## [9601] 0.176 0.175 0.172 0.176 0.177 0.166 0.177 0.183 0.181 0.184 0.181 0.179
## [9613] 0.182 0.185 0.178 0.168 0.166 0.165 0.214 0.235 0.244 0.288 0.329 0.317
## [9625] 0.246 0.238 0.321 0.228 0.299 0.274 0.307 0.241 0.278 0.290 0.271 0.272
## [9637] 0.238 0.424 0.485 0.500 0.509 0.427 0.463 0.462 0.429 0.371 0.330 0.412
## [9649] 0.257 0.241 0.228 0.279 0.284 0.358 0.164 0.164 0.164 0.164 0.164 0.165
## [9661] 0.198 0.368 0.165 0.165 0.485 0.886 0.556 0.570 0.393 0.269 0.194 0.194
## [9673] 0.191 0.189 0.190 0.180 0.189 0.187 0.189 0.192 0.186 0.194 0.190 0.196
## [9685] 0.296 0.296 0.305 0.323 0.250 0.170 0.165 0.167 0.174 0.169 0.186 0.193
## [9697] 0.180 0.165 0.187 0.190 0.181 0.168 0.173 0.171 0.165 0.165 0.167 0.172
## [9709] 0.164 0.164 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.170
## [9721] 0.164 0.166 0.164 0.165 0.170 0.171 0.165 0.165 0.164 0.163 0.166 0.165
## [9733] 0.164 0.183 0.164 0.167 0.173 0.173 0.168 0.255 0.186 0.197 0.181 0.194
## [9745] 0.167 0.169 0.166 0.164 0.171 0.178 0.179 0.166 0.167 0.166 0.236 0.216
## [9757] 0.240 0.235 0.178 0.286 0.270 0.283 0.227 0.178 0.175 0.189 0.243 0.172
## [9769] 0.178 0.173 0.177 0.175 0.184 0.231 0.179 0.194 0.209 0.166 0.180 0.171
## [9781] 0.170 0.177 0.173 0.173 0.182 0.177 0.174 0.174 0.187 0.181 0.169 0.211
## [9793] 0.169 0.197 0.181 0.166 0.185 0.167 0.198 0.166 0.206 0.167 0.204 0.166
## [9805] 0.217 0.166 0.167 0.215 0.264 0.285 0.286 0.282 0.277 0.286 0.276 0.257
## [9817] 0.258 0.307 0.181 0.187 0.175 0.170 0.175 0.171 0.177 0.181 0.168 0.170
## [9829] 0.169 0.168 0.169 0.167 0.170 0.171 0.169 0.169 0.171 0.169 0.170 0.169
## [9841] 0.165 0.404 0.240 0.232 0.293 0.240 0.303 0.320 0.322 0.241 0.244 0.212
## [9853] 0.238 0.290 0.242 0.223 0.207 0.199 0.252 0.255 0.241 0.267 0.285 0.243
## [9865] 0.274 0.213 0.209 0.222 0.253 0.236 0.191 0.208 0.247 0.215 0.172 0.170
## [9877] 0.209 0.265 0.363 0.175 0.277 0.314 0.344 0.247 0.293 0.239 0.243 0.241
## [9889] 0.326 0.337 0.337 0.384 0.286 0.228 0.328 0.179 0.180 0.285 0.168 0.176
## [9901] 0.169 0.319 0.263 0.167 0.293 0.334 0.289 0.302 0.220 0.224 0.176 0.217
## [9913] 0.230 0.247 0.203 0.218 0.209 0.222 0.245 0.241 0.231 0.185 0.170 0.172
## [9925] 0.290 0.198 0.266 0.212 0.238 0.235 0.168 0.181 0.288 0.273 0.302 0.286
## [9937] 0.234 0.266 0.214 0.207 0.164 0.297 0.243 0.222 0.173 0.175 0.166 0.200
## [9949] 0.194 0.191 0.224 0.226 0.189 0.191 0.194 0.176 0.174 0.165 0.171 0.176
## [9961] 0.177 0.167 0.178 0.166 0.176 0.173 0.176 0.164 0.164 0.164 0.164 0.164
## [9973] 0.164 0.282 0.296 0.243 0.316 0.230 0.257 0.299 0.396 0.343 0.327 0.317
## [9985] 0.241 0.275 0.257 0.296 0.261 0.296 0.301 0.280 0.277 0.331 0.287 0.274
## [9997] 0.360 0.322 0.260 0.256 0.303 0.283 0.272 0.189 0.177 0.180 0.206 0.193
## [10009] 0.200 0.192 0.189 0.189 0.188 0.178 0.185 0.205 0.222 0.226 0.188 0.178
## [10021] 0.186 0.196 0.210 0.199 0.189 0.214 0.223 0.219 0.217 0.197 0.191 0.200
## [10033] 0.230 0.191 0.197 0.217 0.216 0.164 0.265 0.243 0.223 0.217 0.189 0.217
## [10045] 0.208 0.246 0.164 0.171 0.170 0.168 0.284 0.266 0.528 0.260 0.396 0.384
## [10057] 0.274 0.207 0.390 0.238 0.210 0.227 0.217 0.205 0.301 0.238 0.262 0.232
## [10069] 0.240 0.263 0.234 0.238 0.253 0.243 0.245 0.222 0.239 0.206 0.164 0.232
## [10081] 0.163 0.235 0.164 0.208 0.163 0.228 0.164 0.236 0.165 0.267 0.164 0.217
## [10093] 0.163 0.226 0.162 0.235 0.164 0.267 0.164 0.378 0.162 0.250 0.163 0.225
## [10105] 0.163 0.562 0.164 0.386 0.165 0.370 0.165 0.408 0.165 0.355 0.165 0.396
## [10117] 0.165 0.351 0.370 0.173 0.184 0.177 0.179 0.192 0.191 0.317 0.214 0.207
## [10129] 0.214 0.254 0.299 0.221 0.285 0.313 0.310 0.217 0.327 0.241 0.233 0.236
## [10141] 0.215 0.167 0.173 0.261 0.238 0.376 0.395 0.332 0.321 0.322 0.358 0.252
## [10153] 0.182 0.195 0.248 0.258 0.202 0.292 0.333 0.320 0.185 0.181 0.255 0.230
## [10165] 0.205 0.275 0.252 0.224 0.203 0.474 0.338 0.361 0.249 0.241 0.268 0.227
## [10177] 0.177 0.179 0.175 0.173 0.229 0.325 0.345 0.327 0.209 0.191 0.177 0.172
## [10189] 0.177 0.165 0.165 0.165 0.165 0.165 0.234 0.252 0.256 0.164 0.199 0.165
## [10201] 0.207 0.165 0.234 0.163 0.266 0.164 0.294 0.164 0.307 0.314 0.245 0.256
## [10213] 0.323 0.343 0.310 0.254 0.164 0.169 0.164 0.194 0.223 0.176 0.165 0.162
## [10225] 0.165 0.163 0.164 0.164 0.165 0.165 0.165 0.163 0.165 0.164 0.165 0.164
## [10237] 0.165 0.164 0.165 0.164 0.165 0.164 0.164 0.165 0.166 0.199 0.215 0.196
## [10249] 0.179 0.186 0.167 0.179 0.253 0.194 0.165 0.275 0.214 0.274 0.169 0.193
## [10261] 0.196 0.190 0.205 0.170 0.183 0.180 0.168 0.181 0.171 0.185 0.239 0.178
## [10273] 0.191 0.164 0.177 0.178 0.173 0.167 0.169 0.179 0.183 0.171 0.178 0.169
## [10285] 0.173 0.173 0.172 0.177 0.172 0.188 0.174 0.202 0.171 0.182 0.176 0.192
## [10297] 0.175 0.189 0.173 0.180 0.180 0.186 0.167 0.184 0.170 0.180 0.179 0.184
## [10309] 0.175 0.221 0.178 0.168 0.168 0.168 0.180 0.170 0.166 0.170 0.168 0.166
## [10321] 0.164 0.165 0.167 0.164 0.169 0.176 0.165 0.166 0.167 0.169 0.169 0.176
## [10333] 0.172 0.181 0.173 0.174 0.172 0.176 0.176 0.207 0.232 0.306 0.324 0.248
## [10345] 0.253 0.275 0.195 0.245 0.214 0.284 0.379 0.275 0.323 0.326 0.372 0.347
## [10357] 0.339 0.325 0.228 0.542 0.365 0.317 0.301 0.336 0.339 0.359 0.249 0.313
## [10369] 0.233 0.196 0.297 0.275 0.185 0.220 0.259 0.435 0.210 0.243 0.187 0.276
## [10381] 0.322 0.353 0.431 0.240 0.371 0.449 0.402 0.427 0.439 0.492 0.459 0.340
## [10393] 0.362 0.316 0.255 0.321 0.343 0.336 0.356 0.357 0.342 0.367 0.352 0.233
## [10405] 0.297 0.204 0.246 0.285 0.377 0.207 0.173 0.169 0.166 0.165 0.166 0.167
## [10417] 0.170 0.165 0.166 0.185 0.164 0.216 0.162 0.255 0.188 0.218 0.197 0.191
## [10429] 0.177 0.192 0.370 0.375 0.167 0.239 0.288 0.305 0.184 0.208 0.183 0.165
## [10441] 0.168 0.169 0.168 0.170 0.185 0.181 0.171 0.192 0.196 0.174 0.174 0.167
## [10453] 0.165 0.164 0.423 0.326 0.259 0.217 0.324 0.301 0.232 0.228 0.305 0.367
## [10465] 0.234 0.232 0.223 0.285 0.266 0.386 0.268 0.209 0.214 0.193 0.221 0.300
## [10477] 0.315 0.296 0.245 0.240 0.351 0.212 0.188 0.199 0.203 0.182 0.201 0.222
## [10489] 0.281 0.288 0.213 0.240 0.258 0.259 0.259 0.235 0.270 0.186 0.252 0.298
## [10501] 0.298 0.268 0.257 0.201 0.203 0.230 0.250 0.238 0.222 0.216 0.162 0.169
## [10513] 0.173 0.163 0.166 0.226 0.162 0.162 0.163 0.254 0.164 0.164 0.162 0.163
## [10525] 0.163 0.163 0.162 0.164 0.164 0.192 0.164 0.162 0.162 0.319 0.379 0.380
## [10537] 0.214 0.167 0.166 0.201 0.176 0.181 0.376 0.395 0.482 0.228 0.175 0.164
## [10549] 0.164 0.163 0.162 0.165 0.163 0.181 0.266 0.181 0.162 0.165 0.165 0.172
## [10561] 0.172 0.184 0.169 0.169 0.175 0.179 0.197 0.190 0.193 0.232 0.203 0.185
## [10573] 0.213 0.177 0.195 0.173 0.169 0.172 0.195 0.212 0.215 0.217 0.244 0.318
## [10585] 0.324 0.270 0.194 0.180 0.176 0.183 0.186 0.223 0.191 0.216 0.333 0.220
## [10597] 0.186 0.198 0.179 0.228 0.253 0.214 0.265 0.228 0.245 0.303 0.183 0.229
## [10609] 0.229 0.396 0.377 0.451 0.727 0.636 0.354 0.359 0.263 0.222 0.289 0.308
## [10621] 0.289 0.290 0.193 0.224 0.237 0.244 0.236 0.217 0.162 0.162 0.162 0.162
## [10633] 0.162 0.164 0.163 0.164 0.162 0.162 0.162 0.162 0.162 0.162 0.162 0.162
## [10645] 0.162 0.162 0.162 0.199 0.164 0.180 0.165 0.203 0.164 0.206 0.162 0.230
## [10657] 0.162 0.368 0.164 0.387 0.164 0.769 0.164 0.364 0.164 0.272 0.164 0.216
## [10669] 0.165 0.331 0.164 0.396 0.164 0.275 0.164 0.215 0.165 0.221 0.164 0.206
## [10681] 0.163 0.360 0.164 0.237 0.164 0.341 0.164 0.162 0.165 0.165 0.164 0.164
## [10693] 0.164 0.162 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.162
## [10705] 0.164 0.164 0.164 0.162 0.162 0.162 0.202 0.266 0.373 0.436 0.609 0.319
## [10717] 0.440 0.432 0.703 0.534 0.489 0.706 0.478 0.867 0.164 0.210 0.273 0.295
## [10729] 0.206 0.260 0.185 0.243 0.239 0.182 0.247 0.234 0.296 0.206 0.226 0.250
## [10741] 0.235 0.378 0.379 0.230 0.312 0.224 0.225 0.338 0.546 0.332 0.309 0.296
## [10753] 0.263 0.377 0.264 0.465 0.497 0.423 0.495 0.514 0.449 0.286 0.399 0.437
## [10765] 0.370 0.269 0.381 0.352 0.164 0.164 0.164 0.371 0.266 0.164 0.275 0.173
## [10777] 0.242 0.181 0.189 0.182 0.164 0.164 0.165 0.206 0.235 0.164 0.265 0.239
## [10789] 0.214 0.250 0.255 0.463 0.254 0.261 0.213 0.217 0.207 0.204 0.202 0.212
## [10801] 0.224 0.224 0.247 0.252 0.256 0.292 0.296 0.227 0.164 0.179 0.164 0.164
## [10813] 0.171 0.309 0.375 0.266 0.597 0.323 0.202 0.334 0.261 0.455 0.403 0.352
## [10825] 0.423 0.369 0.282 0.437 0.359 0.283 0.222 0.213 0.233 0.261 0.165 0.165
## [10837] 0.165 0.175 0.173 0.335 0.214 0.202 0.221 0.331 0.292 0.380 0.750 0.641
## [10849] 0.421 0.460 0.319 0.572 0.200 0.446 0.461 0.448 0.392 0.283 0.255 0.238
## [10861] 0.340 0.257 0.213 0.165 0.165 0.165 0.165 0.165 0.196 0.183 0.269 0.275
## [10873] 0.249 0.625 0.267 0.244 0.243 0.225 0.287 0.271 0.271 0.260 0.283 0.268
## [10885] 0.306 0.209 0.511 0.752 0.422 0.212 0.190 0.193 0.266 0.200 0.328 0.213
## [10897] 0.689 0.367 0.464 0.217 0.570 0.221 0.390 0.303 0.399 0.196 0.377 0.327
## [10909] 0.331 0.242 0.508 0.291 0.569 0.219 0.413 0.263 0.508 0.199 0.429 0.239
## [10921] 0.533 0.167 0.529 0.213 0.435 0.182 0.595 0.562 0.562 0.256 0.335 0.325
## [10933] 0.357 0.333 0.302 0.247 0.213 0.398 0.373 0.277 0.165 0.164 0.172 0.164
## [10945] 0.218 0.190 0.167 0.183 0.211 0.164 0.164 0.194 0.327 0.216 0.367 0.226
## [10957] 0.213 0.235 0.235 0.239 0.259 0.227 0.317 0.236 0.337 0.185 0.239 0.243
## [10969] 0.230 0.272 0.323 0.318 0.305 0.288 0.251 0.260 0.243 0.244 0.353 0.197
## [10981] 0.233 0.176 0.184 0.183 0.198 0.175 0.232 0.168 0.183 0.171 0.167 0.235
## [10993] 0.241 0.270 0.170 0.315 0.164 0.263 0.262 0.205 0.216 0.166 0.168 0.165
## [11005] 0.170 0.196 0.251 0.408 0.413 0.475 0.688 0.394 0.500 0.434 0.489 0.472
## [11017] 0.440 0.623 0.457 0.470 0.488 0.408 0.417 0.499 0.458 0.520 0.449 0.469
## [11029] 0.453 0.210 0.266 0.245 0.221 0.221 0.198 0.357 0.236 0.412 0.293 0.207
## [11041] 0.224 0.191 0.192 0.196 0.244 0.286 0.212 0.247 0.275 0.270 0.286 0.244
## [11053] 0.178 0.187 0.191 0.190 0.243 0.199 0.206 0.190 0.251 0.221 0.246 0.261
## [11065] 0.242 0.217 0.206 0.186 0.184 0.205 0.217 0.207 0.177 0.201 0.192 0.172
## [11077] 0.253 0.190 0.328 0.164 0.366 0.325 0.298 0.269 0.190 0.256 0.232 0.242
## [11089] 0.269 0.425 0.225 0.350 0.243 0.263 0.170 0.164 0.213 0.240 0.215 0.180
## [11101] 0.208 0.266 0.236 0.214 0.228 0.199 0.221 0.258 0.164 0.166 0.176 0.177
## [11113] 0.176 0.168 0.192 0.200 0.173 0.169 0.354 0.274 0.197 0.202 0.164 0.430
## [11125] 0.184 0.187 0.283 0.328 0.320 0.185 0.326 0.186 0.267 0.200 0.219 0.238
## [11137] 0.191 0.256 0.195 0.239 0.370 0.196 0.285 0.252 0.187 0.173 0.164 0.164
## [11149] 0.211 0.182 0.202 0.377 0.335 0.186 0.175 0.245 0.260 0.237 0.202 0.221
## [11161] 0.254 0.246 0.200 0.172 0.237 0.183 0.197 0.181 0.172 0.169 0.173 0.177
## [11173] 0.171 0.167 0.167 0.173 0.172 0.164 0.164 0.199 0.186 0.201 0.162 0.165
## [11185] 0.180 0.191 0.162 0.162 0.163 0.162 0.309 0.190 0.220 0.230 0.163 0.171
## [11197] 0.197 0.305 0.249 0.279 0.275 0.171 0.340 0.279 0.268 0.352 0.427 0.649
## [11209] 0.331 0.465 0.321 0.279 0.228 0.486 0.198 0.253 0.338 0.325 0.269 0.162
## [11221] 0.162 0.163 0.163 0.162 0.162 0.162 0.166 0.181 0.187 0.217 0.206 0.204
## [11233] 0.250 0.185 0.245 0.228 0.202 0.184 0.196 0.192 0.185 0.183 0.204 0.181
## [11245] 0.163 0.163 0.163 0.163 0.165 0.168 0.164 0.166 0.165 0.163 0.163 0.168
## [11257] 0.165 0.477 0.165 0.472 0.165 0.380 0.166 0.311 0.165 0.372 0.410 0.439
## [11269] 0.406 0.468 0.440 0.229 0.219 0.201 0.209 0.165 0.243 0.165 0.163 0.165
## [11281] 0.165 0.165 0.165 0.163 0.165 0.163 0.163 0.163 0.207 0.293 0.332 0.164
## [11293] 0.175 0.358 0.165 0.167 0.162 0.163 0.162 0.164 0.192 0.196 0.190 0.187
## [11305] 0.165 0.168 0.170 0.206 0.288 0.264 0.293 0.169 0.298 0.216 0.284 0.180
## [11317] 0.185 0.174 0.173 0.171 0.164 0.162 0.237 0.314 0.238 0.179 0.171 0.183
## [11329] 0.166 0.352 0.345 0.169 0.228 0.168 0.173 0.285 0.339 0.173 0.363 0.184
## [11341] 0.220 0.267 0.173 0.350 0.169 0.189 0.173 0.173 0.166 0.171 0.167 0.188
## [11353] 0.185 0.203 0.196 0.171 0.189 0.177 0.195 0.174 0.196 0.178 0.200 0.176
## [11365] 0.185 0.180 0.170 0.183 0.195 0.185 0.195 0.190 0.166 0.166 0.163 0.164
## [11377] 0.167 0.166 0.168 0.182 0.201 0.177 0.165 0.163 0.165 0.163 0.164 0.163
## [11389] 0.165 0.166 0.166 0.164 0.163 0.164 0.163 0.165 0.165 0.163 0.166 0.165
## [11401] 0.165 0.166 0.166 0.166 0.168 0.166 0.175 0.175 0.164 0.164 0.191 0.166
## [11413] 0.167 0.168 0.165 0.165 0.165 0.166 0.166 0.167 0.166 0.171 0.170 0.166
## [11425] 0.167 0.170 0.171 0.173 0.166 0.166 0.167 0.166 0.169 0.168 0.213 0.176
## [11437] 0.169 0.169 0.209 0.199 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164
## [11449] 0.164 0.164 0.164 0.166 0.164 0.169 0.164 0.164 0.164 0.164 0.164 0.210
## [11461] 0.301 0.484 0.222 0.189 0.174 0.212 0.222 0.167 0.201 0.213 0.186 0.303
## [11473] 0.194 0.191 0.219 0.206 0.186 0.183 0.170 0.178 0.170 0.169 0.173 0.169
## [11485] 0.167 0.171 0.226 0.286 0.275 0.333 0.326 0.210 0.498 0.356 0.172 0.165
## [11497] 0.166 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164
## [11509] 0.164 0.164 0.164 0.164 0.164 0.178 0.167 0.171 0.175 0.168 0.167 0.167
## [11521] 0.165 0.168 0.166 0.165 0.169 0.166 0.165 0.168 0.165 0.166 0.164 0.164
## [11533] 0.169 0.164 0.164 0.164 0.165 0.165 0.164 0.180 0.179 0.194 0.206 0.191
## [11545] 0.190 0.206 0.231 0.178 0.168 0.165 0.167 0.174 0.176 0.178 0.190 0.192
## [11557] 0.180 0.175 0.191 0.193 0.206 0.184 0.233 0.212 0.190 0.174 0.179 0.187
## [11569] 0.199 0.592 0.164 0.164 0.164 0.191 0.164 0.194 0.199 0.197 0.198 0.195
## [11581] 0.194 0.227 0.183 0.175 0.184 0.167 0.173 0.171 0.166 0.188 0.168 0.202
## [11593] 0.164 0.164 0.164 0.164 0.384 0.184 0.366 0.185 0.227 0.216 0.213 0.315
## [11605] 0.180 0.179 0.187 0.170 0.168 0.169 0.167 0.167 0.197 0.265 0.231 0.222
## [11617] 0.327 0.242 0.327 0.250 0.325 0.209 0.164 0.169 0.165 0.166 0.169 0.177
## [11629] 0.176 0.199 0.345 0.358 0.164 0.170 0.177 0.170 0.176 0.172 0.164 0.164
## [11641] 0.164 0.164 0.164 0.164 0.299 0.380 0.327 0.358 0.329 0.335 0.297 0.312
## [11653] 0.164 0.298 0.262 0.213 0.203 0.195 0.223 0.233 0.217 0.254 0.203 0.207
## [11665] 0.330 0.346 0.293 0.242 0.352 0.342 0.296 0.164 0.164 0.164 0.164 0.193
## [11677] 0.191 0.223 0.204 0.195 0.199 0.168 0.501 0.194 0.198 0.177 0.197 0.409
## [11689] 0.245 0.439 0.458 0.181 0.243 0.220 0.314 0.300 0.351 0.272 0.231 0.255
## [11701] 0.347 0.165 0.165 0.166 0.166 0.179 0.195 0.180 0.169 0.165 0.173 0.166
## [11713] 0.168 0.166 0.166 0.165 0.167 0.168 0.167 0.166 0.165 0.170 0.170 0.169
## [11725] 0.166 0.168 0.166 0.164 0.185 0.174 0.167 0.175 0.164 0.174 0.165 0.164
## [11737] 0.168 0.164 0.167 0.165 0.171 0.168 0.200 0.269 0.177 0.170 0.173 0.164
## [11749] 0.175 0.164 0.486 0.477 0.165 0.164 0.244 0.174 0.162 0.162 0.182 0.227
## [11761] 0.192 0.199 0.202 0.162 0.165 0.173 0.188 0.224 0.246 0.165 0.165 0.188
## [11773] 0.165 0.317 0.307 0.244 0.282 0.187 0.165 0.164 0.182 0.181 0.185 0.225
## [11785] 0.179 0.170 0.178 0.176 0.175 0.166 0.171 0.171 0.175 0.188 0.169 0.169
## [11797] 0.168 0.167 0.170 0.190 0.226 0.211 0.177 0.164 0.164 0.164 0.164 0.164
## [11809] 0.164 0.163 0.164 0.168 0.164 0.164 0.164 0.173 0.164 0.164 0.164 0.164
## [11821] 0.184 0.246 0.320 0.311 0.357 0.249 0.573 0.258 0.275 0.235 0.186 0.176
## [11833] 0.178 0.198 0.172 0.177 0.179 0.168 0.181 0.171 0.170 0.179 0.183 0.218
## [11845] 0.252 0.185 0.164 0.164 0.192 0.164 0.167 0.173 0.187 0.170 0.182 0.178
## [11857] 0.182 0.268 0.177 0.218 0.216 0.173 0.258 0.164 0.165 0.166 0.166 0.164
## [11869] 0.165 0.167 0.166 0.167 0.165 0.166 0.165 0.165 0.167 0.167 0.167 0.175
## [11881] 0.186 0.265 0.189 0.165 0.165 0.164 0.165 0.170 0.164 0.165 0.178 0.202
## [11893] 0.197 0.204 0.218 0.197 0.236 0.233 0.186 0.178 0.195 0.176 0.227 0.223
## [11905] 0.269 0.240 0.214 0.196 0.191 0.191 0.204 0.219 0.204 0.195 0.187 0.190
## [11917] 0.189 0.243 0.168 0.177 0.201 0.182 0.182 0.206 0.180 0.212 0.236 0.272
## [11929] 0.205 0.254 0.210 0.222 0.210 0.184 0.198 0.228 0.221 0.185 0.216 0.492
## [11941] 0.196 0.252 0.268 0.706 0.607 0.201 0.172 0.210 0.206 0.208 0.214 0.203
## [11953] 0.169 0.205 0.289 0.326 0.319 0.188 0.227 0.188 0.210 0.189 0.187 0.164
## [11965] 0.164 0.170 0.169 0.170 0.171 0.169 0.165 0.173 0.171 0.168 0.168 0.166
## [11977] 0.165 0.168 0.165 0.168 0.165 0.167 0.168 0.171 0.171 0.185 0.183 0.176
## [11989] 0.173 0.175 0.168 0.170 0.214 0.189 0.185 0.175 0.211 0.195 0.217 0.236
## [12001] 0.225 0.230 0.168 0.166 0.168 0.170 0.172 0.185 0.181 0.179 0.189 0.191
## [12013] 0.183 0.186 0.181 0.188 0.188 0.274 0.333 0.294 0.256 0.182 0.187 0.201
## [12025] 0.189 0.199 0.178 0.179 0.492 0.263 0.164 0.168 0.165 0.239 0.230 0.246
## [12037] 0.163 0.166 0.166 0.176 0.164 0.165 0.169 0.164 0.166 0.177 0.180 0.536
## [12049] 0.426 0.164 0.172 0.177 0.178 0.163 0.163 0.172 0.341 0.207 0.167 0.167
## [12061] 0.167 0.166 0.165 0.164 0.167 0.167 0.166 0.166 0.165 0.165 0.166 0.165
## [12073] 0.165 0.165 0.165 0.165 0.165 0.166 0.166 0.171 0.168 0.170 0.166 0.168
## [12085] 0.170 0.227 0.263 0.216 0.276 0.286 0.277 0.240 0.241 0.261 0.181 0.259
## [12097] 0.223 0.179 0.283 0.283 0.234 0.208 0.193 0.168 0.165 0.165 0.178 0.211
## [12109] 0.182 0.189 0.185 0.192 0.174 0.321 0.164 0.250 0.269 0.360 0.299 0.222
## [12121] 0.330 0.238 0.330 0.274 0.238 0.320 0.313 0.315 0.407 0.331 0.247 0.316
## [12133] 0.237 0.180 0.178 0.199 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.179
## [12145] 0.164 0.162 0.165 0.164 0.171 0.176 0.216 0.264 0.253 0.181 0.198 0.182
## [12157] 0.178 0.207 0.214 0.240 0.165 0.165 0.164 0.164 0.164 0.163 0.163 0.165
## [12169] 0.165 0.164 0.164 0.164 0.165 0.166 0.165 0.165 0.165 0.165 0.164 0.165
## [12181] 0.165 0.165 0.166 0.165 0.164 0.165 0.166 0.165 0.164 0.165 0.164 0.165
## [12193] 0.164 0.164 0.165 0.165 0.165 0.170 0.165 0.165 0.167 0.166 0.167 0.170
## [12205] 0.166 0.167 0.165 0.166 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [12217] 0.164 0.166 0.166 0.198 0.177 0.193 0.169 0.178 0.164 0.169 0.165 0.166
## [12229] 0.165 0.168 0.165 0.175 0.170 0.173 0.180 0.179 0.184 0.171 0.165 0.167
## [12241] 0.169 0.166 0.164 0.198 0.171 0.180 0.179 0.169 0.217 0.253 0.263 0.187
## [12253] 0.186 0.194 0.173 0.170 0.171 0.175 0.169 0.171 0.177 0.174 0.172 0.175
## [12265] 0.199 0.197 0.183 0.196 0.205 0.188 0.195 0.171 0.163 0.163 0.165 0.167
## [12277] 0.165 0.165 0.164 0.164 0.164 0.165 0.165 0.165 0.164 0.168 0.165 0.165
## [12289] 0.165 0.165 0.164 0.164 0.164 0.166 0.166 0.165 0.166 0.166 0.166 0.165
## [12301] 0.165 0.167 0.165 0.165 0.164 0.405 0.242 0.201 0.254 0.287 0.773 0.373
## [12313] 0.328 0.504 0.182 0.173 0.169 0.168 0.165 0.165 0.315 0.170 0.215 0.165
## [12325] 0.183 0.165 0.167 0.165 0.238 0.318 0.181 0.214 0.251 0.190 0.238 0.347
## [12337] 0.312 0.230 0.182 0.222 0.389 0.372 0.334 0.192 0.281 0.297 0.333 0.249
## [12349] 0.164 0.180 0.185 0.215 0.278 0.232 0.234 0.164 0.164 0.165 0.173 0.175
## [12361] 0.209 0.260 0.164 0.167 0.164 0.172 0.170 0.204 0.202 0.189 0.200 0.168
## [12373] 0.307 0.521 0.209 0.242 0.250 0.260 0.257 0.315 0.164 0.164 0.165 0.166
## [12385] 0.162 0.169 0.173 0.175 0.167 0.166 0.172 0.164 0.163 0.165 0.163 0.167
## [12397] 0.166 0.163 0.165 0.163 0.176 0.196 0.193 0.186 0.172 0.171 0.170 0.175
## [12409] 0.189 0.271 0.663 0.185 0.167 0.166 0.167 0.214 0.289 0.171 0.212 0.184
## [12421] 0.169 0.166 0.182 0.183 0.172 0.177 0.180 0.181 0.170 0.181 0.165 0.163
## [12433] 0.164 0.164 0.164 0.164 0.163 0.163 0.164 0.164 0.164 0.165 0.166 0.164
## [12445] 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.163 0.163 0.163 0.163 0.163
## [12457] 0.163 0.163 0.196 0.164 0.164 0.306 0.241 0.382 0.163 0.239 0.163 0.163
## [12469] 0.168 0.176 0.229 0.233 0.280 0.291 0.220 0.297 0.163 0.164 0.163 0.163
## [12481] 0.163 0.165 0.165 0.164 0.163 0.163 0.163 0.164 0.165 0.164 0.164 0.164
## [12493] 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.164 0.175
## [12505] 0.593 1.032 0.737 0.262 0.164 0.164 0.164 0.164 0.236 0.211 0.238 0.164
## [12517] 0.185 0.221 0.263 0.190 0.189 0.174 0.168 0.303 0.350 0.202 0.162 0.162
## [12529] 0.187 0.162 0.163 0.190 0.260 0.296 0.224 0.184 0.243 0.202 0.180 0.177
## [12541] 0.241 0.181 0.171 0.176 0.174 0.178 0.202 0.178 0.179 0.177 0.304 0.179
## [12553] 0.175 0.176 0.178 0.182 0.172 0.170 0.170 0.176 0.176 0.170 0.172 0.173
## [12565] 0.169 0.173 0.176 0.177 0.172 0.163 0.247 0.196 0.213 0.238 0.226 0.211
## [12577] 0.219 0.165 0.165 0.165 0.165 0.277 0.162 0.162 0.163 0.177 0.165 0.164
## [12589] 0.164 0.164 0.164 0.168 0.190 0.183 0.183 0.182 0.176 0.185 0.172 0.172
## [12601] 0.166 0.184 0.172 0.180 0.176 0.187 0.177 0.176 0.171 0.173 0.168 0.166
## [12613] 0.167 0.168 0.168 0.169 0.173 0.173 0.171 0.175 0.185 0.165 0.169 0.169
## [12625] 0.170 0.174 0.169 0.165 0.171 0.165 0.231 0.177 0.173 0.173 0.172 0.172
## [12637] 0.173 0.171 0.171 0.170 0.172 0.174 0.178 0.173 0.175 0.183 0.207 0.170
## [12649] 0.172 0.170 0.171 0.171 0.294 0.179 0.278 0.186 0.182 0.303 0.172 0.226
## [12661] 0.181 0.282 0.277 0.215 0.176 0.186 0.181 0.186 0.191 0.193 0.184 0.175
## [12673] 0.197 0.166 0.169 0.166 0.170 0.171 0.169 0.170 0.167 0.165 0.167 0.166
## [12685] 0.179 0.172 0.169 0.174 0.182 0.175 0.172 0.167 0.232 0.168 0.232 0.167
## [12697] 0.188 0.166 0.191 0.174 0.199 0.175 0.191 0.175 0.183 0.173 0.181 0.170
## [12709] 0.177 0.173 0.178 0.177 0.174 0.179 0.178 0.188 0.175 0.174 0.171 0.168
## [12721] 0.169 0.203 0.164 0.165 0.164 0.164 0.175 0.172 0.164 0.164 0.183 0.170
## [12733] 0.164 0.170 0.205 0.168 0.170 0.173 0.176 0.175 0.177 0.173 0.169 0.168
## [12745] 0.169 0.171 0.169 0.170 0.171 0.171 0.168 0.166 0.167 0.197 0.164 0.165
## [12757] 0.164 0.164 0.170 0.165 0.176 0.183 0.225 0.296 0.179 0.246 0.179 0.218
## [12769] 0.220 0.192 0.176 0.179 0.172 0.184 0.175 0.170 0.170 0.172 0.172 0.173
## [12781] 0.169 0.170 0.169 0.169 0.170 0.168 0.167 0.172 0.172 0.189 0.184 0.184
## [12793] 0.174 0.178 0.181 0.176 0.171 0.177 0.177 0.174 0.178 0.177 0.187 0.180
## [12805] 0.184 0.180 0.168 0.168 0.215 0.165 0.165 0.167 0.165 0.174 0.177 0.173
## [12817] 0.182 0.185 0.180 0.178 0.186 0.187 0.176 0.175 0.185 0.183 0.184 0.182
## [12829] 0.186 0.188 0.187 0.186 0.180 0.165 0.168 0.196 0.164 0.165 0.166 0.169
## [12841] 0.175 0.174 0.172 0.172 0.176 0.173 0.178 0.179 0.174 0.289 0.177 0.223
## [12853] 0.271 0.247 0.278 0.175 0.248 0.167 0.201 0.313 0.178 0.211 0.170 0.173
## [12865] 0.173 0.170 0.169 0.173 0.171 0.171 0.171 0.187 0.177 0.172 0.171 0.177
## [12877] 0.176 0.182 0.170 0.176 0.172 0.173 0.169 0.165 0.166 0.174 0.172 0.168
## [12889] 0.176 0.172 0.172 0.168 0.173 0.165 0.172 0.171 0.233 0.192 0.184 0.293
## [12901] 0.215 0.282 0.278 0.337 0.255 0.321 0.183 0.168 0.169 0.165 0.181 0.172
## [12913] 0.180 0.191 0.183 0.177 0.176 0.306 0.171 0.176 0.188 0.172 0.172 0.174
## [12925] 0.198 0.212 0.233 0.180 0.203 0.212 0.202 0.182 0.181 0.177 0.179 0.185
## [12937] 0.204 0.189 0.190 0.185 0.197 0.184 0.177 0.187 0.176 0.186 0.182 0.177
## [12949] 0.180 0.177 0.168 0.165 0.163 0.165 0.165 0.165 0.165 0.177 0.168 0.169
## [12961] 0.170 0.171 0.170 0.169 0.168 0.168 0.167 0.172 0.170 0.171 0.171 0.173
## [12973] 0.167 0.168 0.166 0.165 0.165 0.166 0.165 0.165 0.164 0.164 0.165 0.165
## [12985] 0.179 0.185 0.190 0.196 0.186 0.188 0.190 0.172 0.175 0.174 0.181 0.176
## [12997] 0.176 0.177 0.179 0.181 0.189 0.180 0.169 0.170 0.173 0.172 0.172 0.167
## [13009] 0.177 0.178 0.180 0.176 0.181 0.211 0.177 0.218 0.200 0.194 0.215 0.208
## [13021] 0.195 0.194 0.189 0.192 0.192 0.187 0.195 0.179 0.173 0.179 0.186 0.175
## [13033] 0.185 0.180 0.188 0.178 0.189 0.198 0.180 0.178 0.175 0.168 0.171 0.172
## [13045] 0.169 0.169 0.172 0.191 0.186 0.183 0.182 0.184 0.177 0.190 0.174 0.174
## [13057] 0.183 0.187 0.178 0.175 0.182 0.186 0.189 0.186 0.181 0.168 0.175 0.167
## [13069] 0.171 0.169 0.174 0.171 0.174 0.171 0.233 0.181 0.193 0.179 0.179 0.185
## [13081] 0.172 0.194 0.204 0.194 0.204 0.205 0.194 0.195 0.190 0.187 0.185 0.178
## [13093] 0.186 0.186 0.180 0.201 0.193 0.188 0.192 0.178 0.184 0.175 0.185 0.178
## [13105] 0.167 0.176 0.183 0.187 0.184 0.176 0.174 0.176 0.180 0.174 0.193 0.181
## [13117] 0.180 0.187 0.184 0.181 0.176 0.177 0.177 0.177 0.174 0.176 0.179 0.177
## [13129] 0.178 0.179 0.179 0.175 0.186 0.194 0.180 0.190 0.182 0.178 0.188 0.179
## [13141] 0.165 0.166 0.168 0.167 0.168 0.173 0.170 0.172 0.175 0.173 0.171 0.170
## [13153] 0.180 0.171 0.169 0.170 0.169 0.170 0.170 0.170 0.171 0.173 0.169 0.167
## [13165] 0.167 0.168 0.169 0.173 0.168 0.168 0.165 0.168 0.168 0.165 0.165 0.167
## [13177] 0.168 0.169 0.174 0.178 0.175 0.174 0.176 0.171 0.172 0.173 0.171 0.169
## [13189] 0.170 0.168 0.169 0.169 0.170 0.169 0.172 0.172 0.168 0.165 0.166 0.166
## [13201] 0.167 0.166 0.167 0.166 0.169 0.167 0.213 0.169 0.169 0.171 0.176 0.179
## [13213] 0.170 0.210 0.178 0.178 0.186 0.178 0.177 0.173 0.172 0.172 0.169 0.171
## [13225] 0.183 0.177 0.175 0.177 0.185 0.186 0.170 0.182 0.177 0.169 0.181 0.178
## [13237] 0.171 0.171 0.173 0.169 0.170 0.170 0.173 0.175 0.172 0.178 0.176 0.175
## [13249] 0.176 0.172 0.174 0.172 0.172 0.174 0.174 0.173 0.171 0.173 0.174 0.168
## [13261] 0.167 0.172 0.176 0.178 0.170 0.172 0.178 0.173 0.178 0.169 0.174 0.174
## [13273] 0.189 0.178 0.180 0.174 0.181 0.187 0.170 0.231 0.202 0.202 0.195 0.187
## [13285] 0.180 0.181 0.176 0.176 0.170 0.169 0.171 0.174 0.178 0.180 0.182 0.173
## [13297] 0.180 0.186 0.186 0.174 0.177 0.176 0.171 0.173 0.178 0.171 0.171 0.171
## [13309] 0.168 0.172 0.181 0.199 0.186 0.178 0.184 0.178 0.184 0.185 0.174 0.169
## [13321] 0.169 0.173 0.167 0.169 0.171 0.170 0.171 0.169 0.166 0.168 0.171 0.168
## [13333] 0.170 0.173 0.178 0.165 0.167 0.168 0.166 0.165 0.167 0.167 0.167 0.176
## [13345] 0.177 0.175 0.173 0.173 0.175 0.177 0.176 0.171 0.178 0.176 0.172 0.173
## [13357] 0.179 0.175 0.171 0.173 0.178 0.171 0.166 0.166 0.166 0.168 0.165 0.168
## [13369] 0.165 0.167 0.166 0.192 0.199 0.189 0.208 0.199 0.206 0.198 0.184 0.185
## [13381] 0.186 0.183 0.177 0.180 0.186 0.183 0.187 0.188 0.171 0.166 0.176 0.175
## [13393] 0.170 0.166 0.164 0.165 0.165 0.167 0.166 0.168 0.171 0.179 0.176 0.183
## [13405] 0.177 0.176 0.182 0.174 0.173 0.168 0.170 0.168 0.167 0.167 0.168 0.171
## [13417] 0.169 0.169 0.166 0.168 0.167 0.167 0.166 0.167 0.166 0.165 0.166 0.166
## [13429] 0.170 0.197 0.168 0.175 0.180 0.173 0.171 0.173 0.168 0.168 0.169 0.168
## [13441] 0.175 0.170 0.169 0.169 0.176 0.182 0.174 0.170 0.174 0.181 0.175 0.186
## [13453] 0.176 0.186 0.174 0.174 0.179 0.177 0.180 0.180 0.177 0.174 0.174 0.175
## [13465] 0.175 0.176 0.184 0.171 0.175 0.171 0.175 0.172 0.173 0.175 0.172 0.172
## [13477] 0.174 0.171 0.167 0.172 0.173 0.178 0.179 0.182 0.176 0.184 0.186 0.169
## [13489] 0.206 0.204 0.196 0.204 0.203 0.195 0.192 0.182 0.188 0.187 0.192 0.189
## [13501] 0.198 0.189 0.196 0.201 0.190 0.186 0.188 0.186 0.181 0.186 0.181 0.177
## [13513] 0.178 0.180 0.268 0.174 0.180 0.177 0.188 0.180 0.175 0.200 0.191 0.191
## [13525] 0.202 0.205 0.187 0.179 0.175 0.178 0.172 0.177 0.179 0.182 0.179 0.186
## [13537] 0.194 0.195 0.185 0.187 0.183 0.180 0.179 0.177 0.187 0.186 0.183 0.193
## [13549] 0.189 0.196 0.204 0.197 0.185 0.180 0.208 0.203 0.190 0.199 0.211 0.192
## [13561] 0.195 0.178 0.184 0.177 0.183 0.184 0.185 0.187 0.186 0.184 0.186 0.177
## [13573] 0.198 0.180 0.186 0.177 0.180 0.184 0.197 0.181 0.250 0.180 0.194 0.180
## [13585] 0.188 0.178 0.169 0.201 0.179 0.196 0.188 0.186 0.189 0.176 0.178 0.179
## [13597] 0.180 0.181 0.185 0.181 0.186 0.185 0.189 0.185 0.179 0.182 0.188 0.174
## [13609] 0.184 0.176 0.175 0.187 0.185 0.220 0.191 0.220 0.222 0.208 0.225 0.172
## [13621] 0.200 0.174 0.178 0.178 0.208 0.205 0.212 0.225 0.200 0.226 0.226 0.227
## [13633] 0.228 0.224 0.220 0.211 0.190 0.189 0.201 0.207 0.217 0.198 0.203 0.174
## [13645] 0.206 0.204 0.177 0.192 0.190 0.194 0.187 0.203 0.185 0.196 0.187 0.187
## [13657] 0.186 0.189 0.187 0.177 0.178 0.179 0.182 0.183 0.181 0.180 0.180 0.174
## [13669] 0.176 0.181 0.181 0.185 0.179 0.189 0.184 0.190 0.184 0.189 0.183 0.166
## [13681] 0.181 0.165 0.171 0.180 0.197 0.192 0.187 0.193 0.192 0.190 0.182 0.173
## [13693] 0.173 0.185 0.176 0.177 0.181 0.178 0.180 0.181 0.189 0.176 0.173 0.176
## [13705] 0.172 0.171 0.167 0.169 0.162 0.172 0.172 0.466 0.430 0.740 1.139 0.969
## [13717] 0.844 0.886 0.556 0.854 0.610 0.604 0.765 0.594 0.569 0.884 0.932 0.935
## [13729] 1.141 0.929 0.841 0.409 0.743 0.858 0.383 0.482 0.263 0.502 1.032 0.165
## [13741] 0.188 0.334 0.165 0.229 0.165 0.264 0.299 0.282 0.546 0.511 0.470 0.184
## [13753] 0.163 0.218 0.378 0.367 0.194 0.415 0.325 0.394 0.291 0.427 0.530 0.499
## [13765] 0.323 0.165 0.435 0.234 0.191 0.176 0.178 0.182 0.180 0.178 0.171 0.175
## [13777] 0.174 0.170 0.179 0.172 0.171 0.189 0.229 0.200 0.185 0.198 0.165 0.164
## [13789] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [13801] 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.164 0.165 0.164 0.164
## [13813] 0.164 0.164 0.164 0.162 0.162 0.164 0.165 0.173 0.165 0.165 0.164 0.243
## [13825] 0.183 0.184 0.332 0.164 0.164 0.246 0.177 0.258 0.163 0.237 0.164 0.226
## [13837] 0.175 0.467 0.167 0.386 0.164 0.424 0.164 0.389 0.164 0.432 0.164 0.242
## [13849] 0.164 0.164 0.167 0.258 0.222 0.379 0.196 0.289 0.264 0.332 0.314 0.302
## [13861] 0.275 0.278 0.176 0.175 0.181 0.165 0.168 0.169 0.168 0.215 0.175 0.206
## [13873] 0.185 0.432 0.249 0.575 0.471 0.215 0.207 0.202 0.164 0.316 0.323 0.314
## [13885] 0.339 0.198 0.302 0.249 0.228 0.195 0.195 0.240 0.214 0.283 0.347 0.252
## [13897] 0.333 0.258 0.201 0.184 0.177 0.169 0.208 0.200 0.184 0.200 0.190 0.184
## [13909] 0.187 0.166 0.165 0.168 0.165 0.168 0.169 0.170 0.166 0.165 0.166 0.164
## [13921] 0.165 0.166 0.166 0.165 0.231 0.164 0.164 0.164 0.164 0.164 0.165 0.165
## [13933] 0.165 0.165 0.165 0.164 0.164 0.227 0.164 0.165 0.163 0.169 0.164 0.167
## [13945] 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.163
## [13957] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.211 0.164
## [13969] 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.163 0.163 0.163
## [13981] 0.163 0.163 0.162 0.162 0.163 0.164 0.164 0.164 0.164 0.163 0.165 0.164
## [13993] 0.165 0.165 0.164 0.166 0.163 0.162 0.164 0.164 0.165 0.164 0.164 0.219
## [14005] 0.163 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.411 0.168 0.164 0.164
## [14017] 0.165 0.348 0.163 0.163 0.163 0.163 0.163 0.163 0.544 0.181 0.267 0.168
## [14029] 0.453 0.164 0.164 0.163 0.165 0.164 0.165 0.165 0.164 0.163 0.163 0.162
## [14041] 0.164 0.164 0.165 0.164 0.164 0.163 0.163 0.165 0.164 0.164 0.164 0.164
## [14053] 0.166 0.168 0.164 0.164 0.168 0.167 0.163 0.165 0.163 0.166 0.163 0.163
## [14065] 0.162 0.162 0.163 0.164 0.164 0.164 0.164 0.163 0.165 0.164 0.165 0.165
## [14077] 0.164 0.163 0.163 0.162 0.164 0.164 0.165 0.164 0.164 0.324 0.175 0.167
## [14089] 0.164 0.164 0.169 0.164 0.193 0.306 0.254 0.224 0.211 0.197 0.423 0.291
## [14101] 0.163 0.255 0.163 0.332 0.343 0.225 0.228 0.203 0.164 0.164 0.164 0.164
## [14113] 0.168 0.165 0.165 0.164 0.165 0.165 0.164 0.165 0.167 0.166 0.170 0.166
## [14125] 0.165 0.167 0.164 0.172 0.164 0.167 0.165 0.170 0.229 0.164 0.164 0.164
## [14137] 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.164
## [14149] 0.165 0.164 0.164 0.163 0.164 0.164 0.165 0.209 0.164 0.163 0.165 0.164
## [14161] 0.168 0.196 0.210 0.224 0.227 0.215 0.174 0.207 0.194 0.219 0.194 0.218
## [14173] 0.223 0.218 0.196 0.195 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.165
## [14185] 0.165 0.165 0.164 0.220 0.211 0.210 0.193 0.217 0.191 0.332 0.383 0.177
## [14197] 0.182 0.168 0.644 0.195 0.234 0.340 0.168 0.170 0.166 0.168 0.169 0.167
## [14209] 0.170 0.165 0.169 0.164 0.171 0.189 0.171 0.164 0.172 0.182 0.181 0.182
## [14221] 0.167 0.179 0.175 0.167 0.196 0.164 0.168 0.167 0.164 0.164 0.170 0.174
## [14233] 0.173 0.171 0.177 0.168 0.174 0.171 0.182 0.169 0.169 0.167 0.164 0.164
## [14245] 0.167 0.167 0.167 0.180 0.168 0.174 0.169 0.164 0.165 0.164 0.166 0.164
## [14257] 0.173 0.376 0.173 0.283 0.172 0.260 0.165 0.170 0.174 0.166 0.163 0.165
## [14269] 0.170 0.302 0.165 0.164 0.164 0.177 0.163 0.164 0.163 0.164 0.164 0.164
## [14281] 0.164 0.162 0.224 0.187 0.249 0.315 0.343 0.409 0.164 0.165 0.162 0.165
## [14293] 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.163 0.164 0.164 0.165 0.164
## [14305] 0.163 0.164 0.168 0.170 0.172 0.173 0.171 0.173 0.179 0.177 0.174 0.175
## [14317] 0.178 0.171 0.173 0.175 0.182 0.187 0.164 0.164 0.164 0.164 0.164 0.165
## [14329] 0.165 0.165 0.165 0.165 0.164 0.186 0.173 0.173 0.176 0.170 0.222 0.300
## [14341] 0.242 0.275 0.286 0.284 0.250 0.246 0.267 0.238 0.284 0.239 0.215 0.163
## [14353] 0.233 0.163 0.163 0.172 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164
## [14365] 0.165 0.165 0.165 0.164 0.163 0.165 0.164 0.164 0.162 0.165 0.162 0.317
## [14377] 0.164 0.164 0.165 0.164 0.164 0.165 0.163 0.164 0.165 0.163 0.165 0.163
## [14389] 0.165 0.164 0.164 0.162 0.175 0.165 0.177 0.180 0.164 0.197 0.248 0.164
## [14401] 0.164 0.165 0.162 0.165 0.165 0.165 0.165 0.164 0.165 0.164 0.164 0.163
## [14413] 0.164 0.164 0.165 0.164 0.163 0.164 0.224 0.162 0.165 0.164 0.164 0.165
## [14425] 0.163 1.002 1.644 0.609 1.024 0.366 0.281 0.166 0.226 0.233 0.191 0.168
## [14437] 0.163 0.167 0.166 0.167 0.164 0.165 0.165 0.165 0.164 0.164 0.165 0.164
## [14449] 0.164 0.170 0.165 0.201 0.165 0.172 0.166 0.168 0.175 0.163 0.165 0.165
## [14461] 0.165 0.830 1.231 0.465 0.462 0.385 0.407 0.713 0.524 0.486 0.430 1.243
## [14473] 0.934 0.620 0.467 0.293 0.529 0.379 0.707 0.269 0.728 1.108 0.730 1.815
## [14485] 0.214 0.362 0.253 0.588 0.290 0.200 0.168 0.165 0.165 0.164 0.525 1.322
## [14497] 0.271 0.210 0.170 0.373 0.258 0.239 0.520 0.444 0.177 0.172 0.265 0.578
## [14509] 0.164 0.303 0.173 0.165 0.164 0.165 0.165 0.189 0.165 0.316 0.172 0.165
## [14521] 0.165 0.165 0.165 0.191 1.563 1.095 0.164 0.194 0.174 0.165 0.164 0.164
## [14533] 0.164 0.840 0.611 0.164 0.804 0.202 0.195 0.701 0.805 0.219 0.977 0.580
## [14545] 0.452 0.357 0.386 0.165 0.227 0.377 1.584 1.109 0.531 0.722 0.181 0.216
## [14557] 0.191 0.178 0.164 1.350 0.842 0.586 1.062 0.393 0.382 0.564 0.867 0.243
## [14569] 0.861 0.562 0.288 0.745 0.321 0.342 0.238 1.095 0.901 0.456 0.465 0.696
## [14581] 0.641 0.882 0.919 0.854 0.243 0.569 0.299 0.346 0.310 0.533 0.914 1.505
## [14593] 0.782 0.731 0.890 0.904 0.644 0.702 0.430 0.666 0.328 0.429 0.168 0.185
## [14605] 0.236 0.440 0.231 0.170 0.580 0.470 1.003 0.865 0.395 0.891 0.754 0.497
## [14617] 0.604 0.300 0.347 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.163 0.216
## [14629] 0.166 0.180 0.172 0.193 0.202 0.165 0.249 0.167 0.164 0.177 0.164 0.164
## [14641] 0.164 0.173 0.164 0.208 0.170 0.170 0.173 0.167 0.174 0.173 0.251 0.243
## [14653] 0.230 0.306 0.226 0.164 0.167 0.269 0.164 0.165 0.164 0.164 0.164 0.164
## [14665] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.183 0.490 0.165 0.347 0.165
## [14677] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.163 0.164
## [14689] 0.170 0.308 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.164
## [14701] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164
## [14713] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [14725] 0.164 0.164 0.164 0.168 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.166
## [14737] 0.164 0.164 0.164 0.164 0.179 0.182 0.239 0.167 0.164 0.166 0.164 0.176
## [14749] 0.165 0.173 0.165 0.164 0.173 0.164 0.173 0.169 0.174 0.164 0.174 0.164
## [14761] 0.175 0.164 0.174 0.172 0.172 0.175 0.165 0.165 0.165 0.165 0.165 0.165
## [14773] 0.165 0.165 0.165 0.164 0.165 0.164 0.184 0.164 0.295 0.164 0.247 0.173
## [14785] 0.280 0.164 0.212 0.181 0.287 0.195 0.222 0.282 0.270 0.286 0.200 0.263
## [14797] 0.293 0.282 0.251 0.290 0.175 0.274 0.197 0.308 0.174 0.277 0.166 0.289
## [14809] 0.164 0.251 0.164 0.253 0.164 0.280 0.167 0.200 0.166 0.205 0.166 0.219
## [14821] 0.166 0.200 0.166 0.323 0.172 0.277 0.220 0.762 0.499 0.326 0.390 0.200
## [14833] 0.186 0.164 0.164 0.205 0.164 0.272 0.164 0.247 0.218 0.276 0.223 0.232
## [14845] 0.248 0.163 0.231 0.274 0.273 0.215 0.164 0.300 0.171 0.310 0.169 0.237
## [14857] 0.164 0.222 0.164 0.164 0.164 0.164 0.164 0.164 0.166 0.228 0.294 0.291
## [14869] 0.264 0.214 0.215 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [14881] 0.165 0.164 0.163 0.381 0.300 0.372 0.185 0.163 0.164 0.164 0.164 0.164
## [14893] 0.164 0.164 0.168 0.164 0.164 0.164 0.164 0.174 0.739 0.251 0.260 0.178
## [14905] 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [14917] 0.180 0.424 0.337 0.409 0.499 0.531 0.225 0.175 0.166 0.196 0.323 0.164
## [14929] 0.165 0.164 0.164 0.165 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.163
## [14941] 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.165 0.164 0.164
## [14953] 0.165 0.164 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165
## [14965] 0.164 0.164 0.165 0.164 0.163 0.165 0.164 0.163 0.165 0.164 0.164 0.165
## [14977] 0.164 0.165 0.164 0.164 0.165 0.164 0.165 0.164 0.164 0.165 0.164 0.164
## [14989] 0.165 0.164 0.164 0.179 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.164
## [15001] 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163
## [15013] 0.164 0.164 0.163 0.165 0.163 0.164 0.163 0.163 0.164 0.164 0.164 0.164
## [15025] 0.164 0.163 0.164 0.164 0.163 0.163 0.164 0.163 0.164 0.165 0.162 0.164
## [15037] 0.162 0.164 0.162 0.164 0.229 0.164 0.211 0.164 0.164 0.164 0.162 0.164
## [15049] 0.164 0.164 0.165 0.164 0.163 0.165 0.165 0.164 0.164 0.164 0.281 0.164
## [15061] 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.204
## [15073] 0.179 0.164 0.178 0.164 0.164 0.577 0.163 0.737 0.163 0.164 0.164 0.163
## [15085] 0.164 0.165 0.165 0.164 0.164 0.165 0.164 0.165 0.222 0.164 0.165 0.222
## [15097] 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.177 0.164
## [15109] 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.164 0.164 0.163
## [15121] 0.163 0.164 0.163 0.163 0.164 0.164 0.164 0.162 0.165 0.714 0.203 0.683
## [15133] 0.214 0.194 0.199 0.214 0.201 0.230 0.193 0.173 0.166 0.174 0.186 0.209
## [15145] 0.201 0.200 0.185 0.203 0.199 0.192 0.227 0.165 0.165 0.165 0.165 0.165
## [15157] 0.165 0.165 0.165 0.291 0.168 0.174 0.173 0.204 0.211 0.187 0.164 0.197
## [15169] 0.164 0.188 0.163 0.210 0.165 0.207 0.165 0.198 0.163 0.200 0.163 0.213
## [15181] 0.163 0.194 0.164 0.219 0.164 0.220 0.164 0.196 0.164 0.226 0.164 0.194
## [15193] 0.164 0.183 0.164 0.195 0.164 0.205 0.164 0.204 0.163 0.229 0.205 0.177
## [15205] 0.216 0.227 0.223 0.228 0.326 0.364 0.317 0.370 0.165 0.164 0.164 0.163
## [15217] 0.165 0.165 0.163 0.164 0.164 0.164 0.164 0.220 0.200 0.226 0.214 0.203
## [15229] 0.180 0.181 0.220 0.187 0.165 0.200 0.189 0.183 0.173 0.211 0.183 0.196
## [15241] 0.189 0.207 0.205 0.201 0.219 0.185 0.205 0.173 0.189 0.203 0.166 0.175
## [15253] 0.208 0.178 0.170 0.201 0.218 0.172 0.167 0.201 0.214 0.213 0.168 0.184
## [15265] 0.210 0.166 0.165 0.199 0.164 0.164 0.162 0.182 0.164 0.165 0.200 0.173
## [15277] 0.175 0.165 0.200 0.165 0.165 0.167 0.162 0.165 0.165 0.165 0.162 0.165
## [15289] 0.165 0.165 0.162 0.165 0.165 0.167 0.162 0.165 0.165 0.169 0.167 0.165
## [15301] 0.172 0.165 0.169 0.165 0.225 0.164 0.165 0.169 0.164 0.165 0.165 0.163
## [15313] 0.183 0.166 0.165 0.165 0.165 0.177 0.198 0.168 0.203 0.173 0.222 0.169
## [15325] 0.170 0.169 0.164 0.171 0.165 0.165 0.171 0.177 0.164 0.185 0.164 0.164
## [15337] 0.164 0.168 0.177 0.164 0.163 0.164 0.172 0.175 0.169 0.164 0.163 0.177
## [15349] 0.164 0.165 0.165 0.165 0.181 0.165 0.167 0.165 0.165 0.183 0.167 0.165
## [15361] 0.165 0.163 0.195 0.166 0.171 0.165 0.163 0.168 0.165 0.165 0.165 0.163
## [15373] 0.173 0.164 0.175 0.164 0.174 0.165 0.184 0.164 0.172 0.164 0.195 0.164
## [15385] 0.188 0.164 0.205 0.164 0.175 0.164 0.201 0.164 0.166 0.167 0.177 0.164
## [15397] 0.181 0.171 0.221 0.164 0.206 0.165 0.228 0.164 0.174 0.167 0.199 0.164
## [15409] 0.184 0.165 0.192 0.163 0.175 0.167 0.206 0.203 0.195 0.180 0.178 0.216
## [15421] 0.206 0.178 0.185 0.218 0.183 0.168 0.200 0.172 0.164 0.163 0.163 0.165
## [15433] 0.170 0.164 0.222 0.170 0.172 0.172 0.189 0.200 0.180 0.165 0.165 0.212
## [15445] 0.165 0.211 0.215 0.165 0.220 0.187 0.165 0.174 0.189 0.186 0.200 0.211
## [15457] 0.180 0.168 0.165 0.196 0.163 0.191 0.173 0.163 0.184 0.193 0.163 0.178
## [15469] 0.198 0.164 0.164 0.222 0.176 0.164 0.179 0.164 0.293 0.189 0.164 0.211
## [15481] 0.189 0.164 0.268 0.172 0.164 0.238 0.178 0.164 0.205 0.164 0.163 0.214
## [15493] 0.184 0.201 0.210 0.197 0.204 0.213 0.195 0.202 0.210 0.196 0.187 0.203
## [15505] 0.194 0.208 0.194 0.198 0.216 0.165 0.165 0.183 0.163 0.164 0.164 0.164
## [15517] 0.163 0.213 0.202 0.208 0.352 0.355 0.390 0.617 0.857 0.485 0.259 0.295
## [15529] 0.496 0.430 0.270 0.264 0.413 0.430 0.328 0.522 0.197 0.303 0.489 0.406
## [15541] 0.251 0.286 0.211 0.325 0.354 0.165 0.730 0.539 0.759 0.708 0.701 0.200
## [15553] 0.178 0.183 0.235 0.387 0.268 0.164 0.491 0.164 0.279 0.164 1.129 0.164
## [15565] 0.445 0.251 0.619 0.224 0.505 0.343 0.703 0.164 0.328 0.164 0.356 0.409
## [15577] 0.287 0.387 0.360 0.465 0.237 0.164 0.164 0.164 0.164 0.164 0.164 0.163
## [15589] 0.299 0.233 0.233 0.242 0.261 0.269 0.238 0.261 0.221 0.220 0.218 0.193
## [15601] 0.195 0.284 0.219 0.205 0.223 0.269 0.301 0.348 0.303 0.164 0.164 0.164
## [15613] 0.164 0.164 0.164 0.164 0.259 0.228 0.271 0.190 0.255 0.318 0.250 0.675
## [15625] 0.164 0.221 0.460 0.221 0.316 0.447 0.184 0.276 0.245 0.181 0.420 0.407
## [15637] 0.263 0.202 0.500 0.526 0.214 0.195 0.927 0.248 0.198 0.214 0.947 0.311
## [15649] 0.198 0.197 0.374 0.377 0.193 0.196 0.333 0.190 0.228 0.328 0.231 0.198
## [15661] 0.195 0.305 0.484 0.201 0.200 0.351 0.385 0.216 0.205 0.308 0.254 0.192
## [15673] 0.233 0.216 0.410 0.206 0.206 0.492 0.349 0.192 0.193 0.413 0.270 0.181
## [15685] 0.193 0.190 0.413 0.212 0.216 0.324 0.395 0.217 0.188 0.341 0.409 0.177
## [15697] 0.195 0.279 0.389 0.265 0.195 0.494 0.261 0.191 0.195 0.462 0.218 0.177
## [15709] 0.187 0.414 0.412 0.205 0.192 0.376 0.457 0.197 0.193 0.368 0.223 0.374
## [15721] 0.250 0.570 0.375 0.412 0.277 0.549 0.342 0.550 0.288 0.803 0.165 0.189
## [15733] 0.162 0.473 0.434 0.288 0.397 0.373 0.212 0.294 0.330 0.262 0.839 0.312
## [15745] 0.219 0.420 0.355 0.279 0.228 0.165 0.289 0.195 0.238 0.186 0.230 0.245
## [15757] 0.650 0.199 0.240 0.165 0.458 0.326 0.380 0.522 0.207 0.175 0.373 0.608
## [15769] 0.563 0.186 0.308 0.532 0.462 0.217 0.229 0.796 0.473 0.176 0.275 0.829
## [15781] 0.270 0.184 0.194 0.467 0.520 0.194 0.195 0.554 0.339 0.164 0.195 0.587
## [15793] 0.671 0.192 0.203 0.422 0.413 0.178 0.196 0.362 0.257 0.207 0.205 0.341
## [15805] 0.557 0.177 0.186 0.354 0.220 0.165 0.214 0.329 0.266 0.202 0.201 0.195
## [15817] 0.212 0.245 0.164 0.205 0.164 0.187 0.212 0.236 0.164 0.199 0.164 0.216
## [15829] 0.226 0.213 0.165 0.205 0.165 0.210 0.165 0.224 0.165 0.183 0.165 0.222
## [15841] 0.192 0.298 0.165 0.188 0.352 0.179 0.606 0.271 0.220 0.235 0.164 0.231
## [15853] 0.242 0.266 0.253 0.242 0.229 0.244 0.443 0.320 0.366 0.819 0.439 0.838
## [15865] 0.272 0.547 0.490 0.965 0.690 0.788 0.603 0.533 0.607 0.732 0.709 0.773
## [15877] 0.405 0.754 0.432 0.524 0.630 0.449 0.300 0.164 0.215 0.256 0.201 0.307
## [15889] 0.264 0.518 0.366 0.190 0.292 0.425 0.215 0.323 0.202 0.187 0.164 0.234
## [15901] 0.182 0.422 0.198 0.180 0.535 0.249 0.173 0.528 0.253 0.164 0.674 0.214
## [15913] 0.191 0.714 0.220 0.182 0.403 0.257 0.312 0.210 0.174 0.172 0.308 0.219
## [15925] 0.192 0.181 0.482 0.163 0.188 0.406 0.343 0.357 0.326 0.286 0.195 0.351
## [15937] 0.188 0.299 0.164 0.299 0.328 0.299 0.163 0.164 0.361 0.164 0.164 0.235
## [15949] 0.164 0.405 0.183 0.513 0.301 0.375 0.261 0.252 0.202 0.457 0.417 0.826
## [15961] 0.425 0.546 0.740 0.615 0.524 0.521 0.627 0.314 0.671 0.724 0.358 0.237
## [15973] 0.477 0.185 0.283 0.226 0.236 0.217 0.164 0.365 0.283 0.330 0.211 0.434
## [15985] 0.309 0.165 0.322 0.299 0.164 0.165 0.164 0.171 0.184 0.164 0.174 0.190
## [15997] 0.164 0.165 0.274 0.164 0.178 0.191 0.164 0.175 0.192 0.163 0.164 0.197
## [16009] 0.306 0.165 0.204 0.202 0.165 0.176 0.169 0.164 0.177 0.201 0.165 0.173
## [16021] 0.212 0.165 0.608 0.227 0.164 0.286 0.210 0.164 0.479 0.176 0.165 0.508
## [16033] 0.197 0.165 0.342 0.169 0.164 0.173 0.171 0.164 0.627 0.164 0.164 0.168
## [16045] 0.168 0.165 0.291 0.164 0.167 0.164 0.436 0.167 0.164 0.165 0.560 0.167
## [16057] 0.165 0.176 0.524 0.164 0.173 0.165 0.179 0.595 0.172 0.195 0.171 0.189
## [16069] 0.165 0.201 0.542 0.308 0.198 0.172 0.187 0.203 0.182 0.615 0.213 0.624
## [16081] 0.232 0.170 0.194 0.183 0.171 0.214 0.270 0.186 0.179 0.378 0.175 0.176
## [16093] 0.167 0.164 0.184 0.172 0.164 0.194 0.168 0.164 0.302 0.171 0.164 0.311
## [16105] 0.177 0.164 0.220 0.187 0.164 0.198 0.273 0.164 0.199 0.173 0.178 0.162
## [16117] 0.177 0.164 0.169 0.170 0.165 0.174 0.165 0.176 0.170 0.169 0.177 0.172
## [16129] 0.173 0.185 0.167 0.175 0.164 0.165 0.186 0.164 0.184 0.223 0.164 0.164
## [16141] 0.187 0.164 0.165 0.168 0.165 0.165 0.189 0.171 0.168 0.174 0.195 0.185
## [16153] 0.165 0.174 0.177 0.174 0.173 0.166 0.166 0.166 0.165 0.166 0.168 0.166
## [16165] 0.167 0.175 0.183 0.184 0.168 0.164 0.165 0.166 0.166 0.167 0.166 0.166
## [16177] 0.167 0.165 0.166 0.165 0.165 0.165 0.165 0.165 0.168 0.164 0.168 0.164
## [16189] 0.165 0.165 0.165 0.166 0.164 0.165 0.165 0.165 0.165 0.165 0.170 0.165
## [16201] 0.163 0.165 0.165 0.163 0.164 0.165 0.165 0.165 0.165 0.165 0.164 0.163
## [16213] 0.165 0.165 0.164 0.165 0.167 0.170 0.176 0.187 0.191 0.198 0.200 0.209
## [16225] 0.176 0.173 0.167 0.167 0.176 0.174 0.171 0.171 0.182 0.178 0.174 0.165
## [16237] 0.165 0.164 0.165 0.164 0.172 0.169 0.165 0.163 0.165 0.178 0.168 0.166
## [16249] 0.166 0.164 0.164 0.164 0.168 0.167 0.166 0.166 0.166 0.166 0.166 0.165
## [16261] 0.166 0.167 0.165 0.163 0.168 0.171 0.164 0.168 0.217 0.183 0.233 0.233
## [16273] 0.213 0.236 0.236 0.230 0.241 0.285 0.257 0.274 0.247 0.244 0.287 0.264
## [16285] 0.253 0.400 0.234 0.246 0.497 0.166 0.165 0.165 0.166 0.163 0.165 0.172
## [16297] 0.180 0.180 0.193 0.192 0.180 0.179 0.186 0.169 0.169 0.171 0.170 0.168
## [16309] 0.173 0.171 0.177 0.186 0.164 0.164 0.165 0.164 0.162 0.183 0.194 0.200
## [16321] 0.163 0.164 0.165 0.177 0.179 0.166 0.185 0.186 0.164 0.185 0.171 0.213
## [16333] 0.168 0.200 0.174 0.193 0.186 0.261 0.164 0.164 0.164 0.174 0.164 0.164
## [16345] 0.164 0.165 0.165 0.165 0.165 0.178 0.188 0.205 0.196 0.190 0.197 0.172
## [16357] 0.170 0.166 0.166 0.167 0.164 0.166 0.163 0.166 0.165 0.164 0.167 0.165
## [16369] 0.165 0.199 0.192 0.164 0.164 0.179 0.173 0.184 0.170 0.175 0.186 0.187
## [16381] 0.185 0.193 0.171 0.167 0.165 0.164 0.165 0.178 0.180 0.188 0.188 0.213
## [16393] 0.209 0.203 0.191 0.167 0.174 0.164 0.164 0.172 0.171 0.180 0.183 0.181
## [16405] 0.188 0.179 0.180 0.186 0.194 0.200 0.170 0.189 0.180 0.180 0.190 0.263
## [16417] 0.178 0.187 0.188 0.182 0.184 0.182 0.183 0.187 0.187 0.177 0.184 0.182
## [16429] 0.179 0.182 0.183 0.183 0.187 0.193 0.183 0.201 0.193 0.190 0.192 0.199
## [16441] 0.183 0.203 0.216 0.181 0.176 0.195 0.180 0.166 0.177 0.174 0.179 0.208
## [16453] 0.206 0.213 0.218 0.209 0.189 0.188 0.233 0.233 0.273 0.178 0.202 0.212
## [16465] 0.234 0.179 0.219 0.237 0.219 0.243 0.211 0.241 0.205 0.207 0.198 0.199
## [16477] 0.200 0.216 0.204 0.209 0.213 0.209 0.202 0.201 0.165 0.186 0.208 0.164
## [16489] 0.196 0.206 0.182 0.206 0.227 0.199 0.222 0.253 0.252 0.202 0.235 0.200
## [16501] 0.197 0.211 0.196 0.211 0.236 0.184 0.231 0.228 0.219 0.249 0.240 0.191
## [16513] 0.199 0.266 0.195 0.266 0.245 0.264 0.277 0.272 0.202 0.190 0.216 0.201
## [16525] 0.228 0.232 0.212 0.268 0.254 0.239 0.232 0.262 0.231 0.275 0.245 0.231
## [16537] 0.247 0.241 0.266 0.250 0.257 0.265 0.217 0.324 0.251 0.221 0.206 0.218
## [16549] 0.240 0.191 0.193 0.168 0.204 0.234 0.216 0.212 0.274 0.275 0.270 0.253
## [16561] 0.284 0.255 0.227 0.260 0.283 0.207 0.206 0.242 0.209 0.205 0.256 0.223
## [16573] 0.266 0.238 0.200 0.277 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [16585] 0.165 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [16597] 0.164 0.165 0.165 0.165 0.165 0.190 0.164 0.191 0.165 0.164 0.165 0.165
## [16609] 0.165 0.165 0.181 0.183 0.165 0.178 0.165 0.187 0.165 0.188 0.165 0.184
## [16621] 0.163 0.179 0.165 0.180 0.165 0.182 0.165 0.182 0.165 0.248 0.163 0.280
## [16633] 0.165 0.413 0.165 0.481 0.164 0.351 0.420 0.429 0.165 0.407 0.435 0.434
## [16645] 0.436 0.455 0.424 0.164 0.411 0.407 0.444 0.293 0.335 0.435 0.402 0.424
## [16657] 0.384 0.417 0.403 0.362 0.196 0.221 0.164 0.230 0.164 0.188 0.190 0.180
## [16669] 0.175 0.188 0.198 0.165 0.191 0.254 0.180 0.187 0.186 0.172 0.181 0.165
## [16681] 0.187 0.163 0.196 0.163 0.182 0.215 0.182 0.205 0.182 0.165 0.173 0.168
## [16693] 0.170 0.187 0.229 0.216 0.219 0.229 0.231 0.220 0.196 0.164 0.164 0.164
## [16705] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.162 0.164 0.164
## [16717] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.166 0.165 0.164 0.164
## [16729] 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165
## [16741] 0.164 0.165 0.164 0.164 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165
## [16753] 0.164 0.164 0.164 0.164 0.162 0.165 0.164 0.164 0.164 0.164 0.164 0.164
## [16765] 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.164 0.164 0.164
## [16777] 0.165 0.163 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.165 0.165
## [16789] 0.164 0.164 0.164 0.164 0.164 0.179 0.164 0.169 0.165 0.165 0.164 0.165
## [16801] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.165 0.165 0.204
## [16813] 0.182 0.184 0.168 0.197 0.171 0.169 0.175 0.185 0.181 0.198 0.179 0.173
## [16825] 0.181 0.182 0.169 0.220 0.205 0.186 0.397 0.189 0.338 0.171 0.274 0.170
## [16837] 0.314 0.182 0.280 0.188 0.337 0.204 0.324 0.207 0.376 0.203 0.387 0.213
## [16849] 0.288 0.221 0.267 0.237 0.470 0.187 0.411 0.209 0.489 0.183 0.240 0.233
## [16861] 0.191 0.380 0.231 0.232 0.235 0.189 0.251 0.329 0.233 0.301 0.236 0.226
## [16873] 0.192 0.178 0.206 0.180 0.184 0.169 0.176 0.169 0.196 0.169 0.203 0.179
## [16885] 0.177 0.187 0.199 0.194 0.236 0.207 0.196 0.199 0.189 0.167 0.216 0.175
## [16897] 0.225 0.178 0.198 0.172 0.194 0.164 0.164 0.189 0.190 0.164 0.164 0.360
## [16909] 0.316 0.270 0.362 0.292 0.354 0.387 0.287 0.392 0.219 0.501 0.354 0.589
## [16921] 0.366 0.251 0.556 0.522 0.425 0.462 0.308 0.813 0.545 0.616 0.279 0.794
## [16933] 0.459 0.540 0.334 0.665 0.466 0.590 0.574 0.751 0.484 0.803 0.191 0.384
## [16945] 0.457 0.559 0.469 0.549 0.668 0.277 0.688 0.367 0.802 0.484 0.598 1.067
## [16957] 0.412 0.553 0.361 0.358 0.681 0.420 0.822 0.579 0.424 0.523 0.532 1.013
## [16969] 0.768 0.848 0.835 0.852 0.909 0.797 0.322 0.415 0.682 0.515 0.372 0.461
## [16981] 0.386 0.297 0.268 0.263 0.280 0.537 0.257 0.263 0.352 0.278 0.172 0.285
## [16993] 0.461 0.251 0.439 0.276 0.557 0.522 0.550 0.206 0.588 0.675 0.680 0.728
## [17005] 0.476 0.418 0.498 0.400 0.343 0.267 0.287 0.394 0.409 0.198 0.165 0.165
## [17017] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.172 0.216 0.229 0.250 0.236
## [17029] 0.242 0.237 0.228 0.255 0.265 0.329 0.164 0.163 0.283 0.164 0.297 0.437
## [17041] 0.618 0.309 0.271 0.222 0.302 0.445 1.277 1.229 0.597 1.118 2.046 0.811
## [17053] 0.754 0.401 0.248 0.283 0.378 0.204 0.193 0.196 0.208 0.271 0.309 0.351
## [17065] 0.313 0.195 0.303 0.364 0.438 0.353 0.324 0.491 0.307 0.414 0.332 0.481
## [17077] 0.399 0.546 0.202 0.479 0.316 0.584 0.573 0.601 0.721 0.826 0.333 0.465
## [17089] 0.204 0.305 0.351 0.266 0.231 0.197 0.240 0.330 0.303 0.319 0.324 0.393
## [17101] 0.277 0.409 0.209 0.535 0.391 0.563 0.530 0.417 0.542 0.559 0.342 0.475
## [17113] 0.341 0.737 0.471 0.651 0.297 0.845 0.463 0.593 0.314 0.623 0.486 0.596
## [17125] 0.599 0.884 0.510 0.624 0.222 0.351 0.501 0.661 0.577 0.561 0.388 0.676
## [17137] 0.929 0.589 0.576 0.586 0.842 1.132 0.374 0.459 0.889 0.533 0.434 0.553
## [17149] 1.126 1.143 0.315 0.712 0.931 0.776 0.548 0.384 0.535 0.487 0.347 0.841
## [17161] 0.256 0.416 0.279 0.362 0.244 0.300 0.496 0.330 0.249 0.229 0.455 0.212
## [17173] 0.301 0.284 0.355 0.340 0.489 0.517 0.345 0.485 0.525 0.728 0.634 0.789
## [17185] 0.875 0.625 0.529 0.631 0.511 0.362 0.580 0.889 0.696 0.823 0.681 0.635
## [17197] 0.451 0.350 0.193 0.213 0.181 0.242 0.199 0.262 0.205 0.294 0.267 0.259
## [17209] 0.193 0.291 0.174 0.479 0.181 0.449 0.179 0.300 0.200 0.287 0.219 0.306
## [17221] 0.251 0.253 0.163 0.165 0.229 0.164 0.192 0.229 0.391 0.610 0.375 0.462
## [17233] 0.394 1.086 0.431 0.607 0.358 0.598 0.301 0.235 2.022 1.748 0.431 0.702
## [17245] 0.732 0.983 0.395 0.230 0.499 0.284 0.207 0.248 0.252 0.211 0.229 0.238
## [17257] 0.223 0.237 0.204 0.209 0.226 0.218 1.275 0.163 0.163 0.163 0.163 0.164
## [17269] 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164
## [17281] 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.164 0.163
## [17293] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163
## [17305] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163
## [17317] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163
## [17329] 0.163 0.163 0.164 0.163 0.163 0.163 0.216 0.163 0.163 0.163 0.164 0.163
## [17341] 0.163 0.163 0.195 0.163 0.163 0.163 0.182 0.163 0.163 0.163 0.190 0.163
## [17353] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.217 0.163
## [17365] 0.163 0.163 0.164 0.163 0.163 0.163 0.194 0.163 0.163 0.178 0.395 0.163
## [17377] 0.163 0.232 0.183 0.164 0.163 0.382 0.225 0.163 0.163 0.312 0.269 0.164
## [17389] 0.174 0.163 0.203 0.163 0.250 0.356 0.340 0.231 0.164 0.272 0.290 0.164
## [17401] 0.223 0.351 0.163 0.164 0.163 0.163 0.279 0.163 0.326 0.163 0.163 0.484
## [17413] 0.163 0.163 0.163 0.163 0.182 0.163 0.251 0.163 0.263 0.182 0.163 0.163
## [17425] 0.187 0.169 0.163 0.163 0.178 0.163 0.163 0.163 0.253 0.163 0.163 0.163
## [17437] 0.228 0.163 0.163 0.163 0.330 0.163 0.163 0.163 0.294 0.163 0.163 0.163
## [17449] 0.171 0.163 0.163 0.163 0.174 0.163 0.163 0.163 0.253 0.163 0.272 0.163
## [17461] 0.164 0.163 0.253 0.256 0.190 0.163 0.197 0.163 0.164 0.163 0.163 0.163
## [17473] 0.164 0.163 0.163 0.164 0.322 0.164 0.164 0.163 0.164 0.164 0.163 0.321
## [17485] 0.164 0.187 0.163 0.220 0.163 0.164 0.177 0.318 0.164 0.230 0.164 0.164
## [17497] 0.173 0.163 0.164 0.332 0.186 0.183 0.164 0.163 0.199 0.166 0.164 0.163
## [17509] 0.174 0.175 0.332 0.163 0.333 0.163 0.164 0.204 0.164 0.163 0.164 0.164
## [17521] 0.177 0.176 0.164 0.164 0.218 0.329 0.164 0.164 0.190 0.327 0.164 0.329
## [17533] 0.164 0.167 0.208 0.316 0.164 0.164 0.163 0.163 0.240 0.164 0.163 0.163
## [17545] 0.333 0.295 0.163 0.314 0.163 0.175 0.177 0.164 0.163 0.176 0.163 0.195
## [17557] 0.322 0.251 0.311 0.163 0.163 0.322 0.164 0.163 0.163 0.329 0.164 0.195
## [17569] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.319 0.163 0.330 0.253 0.163
## [17581] 0.163 0.379 0.164 0.163 0.163 0.212 0.163 0.163 0.183 0.164 0.177 0.163
## [17593] 0.194 0.170 0.163 0.329 0.323 0.164 0.164 0.164 0.164 0.164 0.164 0.187
## [17605] 0.164 0.164 0.176 0.206 0.229 0.164 0.169 0.164 0.196 0.255 0.164 0.222
## [17617] 0.383 0.199 0.260 0.164 0.164 0.202 0.490 0.164 0.211 0.569 0.163 0.164
## [17629] 0.304 0.291 0.164 0.331 0.174 0.351 0.241 0.343 0.163 0.164 0.164 0.188
## [17641] 0.322 0.164 0.164 0.180 0.163 0.164 0.164 0.164 0.164 0.164 0.163 0.322
## [17653] 0.316 0.164 0.164 0.163 0.164 0.324 0.176 0.164 0.163 0.273 0.163 0.164
## [17665] 0.163 0.164 0.163 0.164 0.164 0.163 0.207 0.163 0.314 0.163 0.163 0.163
## [17677] 0.163 0.163 0.214 0.163 0.164 0.163 0.164 0.163 0.270 0.163 0.227 0.163
## [17689] 0.178 0.171 0.164 0.169 0.288 0.163 0.163 0.163 0.279 0.163 0.197 0.163
## [17701] 0.240 0.332 0.164 0.178 0.321 0.163 0.331 0.172 0.461 0.164 0.282 0.164
## [17713] 0.472 0.181 0.247 0.196 0.164 0.163 0.211 0.163 0.170 0.292 0.306 0.243
## [17725] 0.268 0.231 0.443 0.163 0.164 0.400 0.246 0.351 0.356 0.163 0.239 0.387
## [17737] 0.164 0.327 0.332 0.163 0.164 0.164 0.190 0.164 0.164 0.164 0.164 0.164
## [17749] 0.164 0.163 0.215 0.164 0.163 0.163 0.163 0.164 0.163 0.241 0.164 0.163
## [17761] 0.324 0.164 0.164 0.163 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.184
## [17773] 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.191 0.164 0.199 0.329
## [17785] 0.163 0.164 0.164 0.181 0.163 0.164 0.164 0.163 0.163 0.330 0.164 0.163
## [17797] 0.163 0.264 0.164 0.178 0.163 0.164 0.212 0.163 0.164 0.319 0.164 0.163
## [17809] 0.163 0.185 0.270 0.163 0.306 0.176 0.164 0.172 0.250 0.221 0.250 0.163
## [17821] 0.163 0.330 0.378 0.163 0.163 0.211 0.463 0.163 0.164 0.163 0.204 0.204
## [17833] 0.163 0.163 0.286 0.164 0.163 0.331 0.172 0.231 0.163 0.294 0.289 0.196
## [17845] 0.163 0.247 0.229 0.163 0.388 0.326 0.306 0.172 0.164 0.163 0.163 0.164
## [17857] 0.312 0.164 0.335 0.164 0.164 0.305 0.222 0.164 0.377 0.164 0.164 0.171
## [17869] 0.203 0.329 0.164 0.296 0.178 0.164 0.164 0.299 0.164 0.427 0.394 0.164
## [17881] 0.164 0.207 0.461 0.207 0.306 0.164 0.164 0.312 0.164 0.164 0.250 0.317
## [17893] 0.164 0.164 0.256 0.637 0.163 0.288 0.415 0.242 0.193 0.499 0.323 0.164
## [17905] 0.331 0.412 0.164 0.283 0.327 0.163 0.164 0.164 0.344 0.238 0.164 0.175
## [17917] 0.186 0.289 0.164 0.164 0.164 0.164 0.163 0.164 0.163 0.163 0.164 0.163
## [17929] 0.164 0.164 0.163 0.164 0.163 0.163 0.164 0.163 0.318 0.164 0.163 0.164
## [17941] 0.164 0.163 0.238 0.268 0.164 0.163 0.176 0.163 0.164 0.163 0.164 0.163
## [17953] 0.164 0.163 0.186 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.222 0.163
## [17965] 0.164 0.163 0.164 0.163 0.164 0.245 0.164 0.177 0.164 0.164 0.244 0.164
## [17977] 0.164 0.327 0.171 0.163 0.163 0.164 0.369 0.229 0.163 0.164 0.163 0.163
## [17989] 0.179 0.164 0.163 0.163 0.163 0.236 0.163 0.184 0.163 0.178 0.247 0.164
## [18001] 0.163 0.164 0.331 0.236 0.163 0.331 0.190 0.163 0.287 0.327 0.222 0.163
## [18013] 0.392 0.163 0.164 0.163 0.163 0.163 0.164 0.234 0.163 0.163 0.255 0.164
## [18025] 0.182 0.489 0.163 0.325 0.348 0.170 0.170 0.244 0.324 0.186 0.163 0.311
## [18037] 0.261 0.349 0.487 0.279 0.163 0.164 0.163 0.331 0.207 0.270 0.229 0.164
## [18049] 0.481 0.164 0.212 0.164 0.427 0.164 0.348 0.321 0.176 0.248 0.261 0.183
## [18061] 0.206 0.164 0.168 0.327 0.254 0.164 0.330 0.256 0.246 0.278 0.180 0.357
## [18073] 0.318 0.221 0.320 0.164 0.250 0.204 0.174 0.332 0.171 0.317 0.337 0.238
## [18085] 0.220 0.163 0.175 0.164 0.163 0.250 0.511 0.211 0.163 0.163 0.164 0.194
## [18097] 0.163 0.163 0.471 0.164 0.164 0.203 0.294 0.164 0.222 0.296 0.163 0.164
## [18109] 0.164 0.163 0.164 0.214 0.164 0.164 0.327 0.164 0.322 0.164 0.163 0.164
## [18121] 0.163 0.164 0.198 0.164 0.164 0.171 0.164 0.164 0.184 0.164 0.164 0.163
## [18133] 0.164 0.164 0.176 0.164 0.164 0.301 0.239 0.164 0.164 0.166 0.164 0.164
## [18145] 0.177 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.197
## [18157] 0.163 0.164 0.164 0.194 0.163 0.164 0.164 0.313 0.163 0.164 0.164 0.323
## [18169] 0.163 0.164 0.164 0.179 0.163 0.164 0.164 0.163 0.164 0.164 0.172 0.163
## [18181] 0.164 0.164 0.164 0.250 0.263 0.164 0.200 0.163 0.164 0.215 0.164 0.163
## [18193] 0.164 0.335 0.369 0.163 0.164 0.163 0.295 0.238 0.164 0.163 0.214 0.164
## [18205] 0.164 0.332 0.164 0.164 0.164 0.206 0.475 0.164 0.164 0.233 0.163 0.164
## [18217] 0.164 0.164 0.163 0.164 0.164 0.164 0.183 0.164 0.164 0.227 0.163 0.164
## [18229] 0.164 0.283 0.163 0.164 0.164 0.313 0.204 0.164 0.164 0.179 0.164 0.164
## [18241] 0.164 0.190 0.179 0.164 0.164 0.186 0.345 0.164 0.164 0.197 0.164 0.164
## [18253] 0.164 0.398 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.250
## [18265] 0.164 0.164 0.164 0.175 0.164 0.164 0.164 0.164 0.333 0.164 0.164 0.329
## [18277] 0.270 0.164 0.164 0.195 0.164 0.164 0.164 0.234 0.164 0.164 0.164 0.201
## [18289] 0.164 0.164 0.164 0.219 0.381 0.164 0.225 0.177 0.480 0.164 0.231 0.439
## [18301] 0.164 0.164 0.260 0.323 0.164 0.164 0.481 0.164 0.164 0.221 0.222 0.164
## [18313] 0.164 0.195 0.181 0.164 0.164 0.164 0.402 0.164 0.164 0.328 0.164 0.164
## [18325] 0.304 0.164 0.188 0.164 0.326 0.316 0.164 0.313 0.163 0.243 0.164 0.163
## [18337] 0.170 0.175 0.163 0.350 0.163 0.163 0.330 0.183 0.163 0.214 0.164 0.163
## [18349] 0.164 0.164 0.163 0.164 0.164 0.163 0.164 0.164 0.163 0.163 0.173 0.314
## [18361] 0.163 0.163 0.163 0.163 0.163 0.344 0.163 0.193 0.163 0.232 0.163 0.164
## [18373] 0.163 0.163 0.324 0.163 0.163 0.220 0.163 0.163 0.174 0.164 0.163 0.163
## [18385] 0.164 0.164 0.163 0.163 0.164 0.304 0.163 0.163 0.185 0.163 0.324 0.163
## [18397] 0.385 0.163 0.164 0.332 0.167 0.163 0.164 0.325 0.179 0.163 0.164 0.163
## [18409] 0.163 0.246 0.164 0.163 0.196 0.164 0.227 0.163 0.224 0.205 0.163 0.163
## [18421] 0.164 0.163 0.302 0.179 0.164 0.163 0.253 0.257 0.190 0.164 0.194 0.164
## [18433] 0.430 0.163 0.163 0.397 0.164 0.183 0.163 0.163 0.164 0.163 0.167 0.163
## [18445] 0.332 0.190 0.315 0.339 0.164 0.360 0.331 0.253 0.164 0.211 0.163 0.164
## [18457] 0.175 0.164 0.163 0.163 0.211 0.184 0.164 0.163 0.318 0.163 0.164 0.164
## [18469] 0.163 0.163 0.194 0.163 0.163 0.186 0.171 0.163 0.163 0.237 0.163 0.163
## [18481] 0.163 0.244 0.163 0.323 0.163 0.163 0.163 0.500 0.163 0.163 0.250 0.163
## [18493] 0.163 0.219 0.164 0.189 0.323 0.215 0.193 0.189 0.238 0.330 0.164 0.164
## [18505] 0.329 0.163 0.164 0.164 0.331 0.322 0.164 0.163 0.164 0.163 0.163 0.164
## [18517] 0.164 0.186 0.174 0.175 0.204 0.169 0.170 0.166 0.164 0.183 0.164 0.238
## [18529] 0.207 0.172 0.164 0.170 0.164 0.164 0.168 0.164 0.164 0.172 0.170 0.164
## [18541] 0.164 0.229 0.175 0.176 0.164 0.316 0.169 0.172 0.164 0.456 0.167 0.164
## [18553] 0.164 0.366 0.168 0.164 0.164 0.190 0.164 0.164 0.164 0.186 0.164 0.164
## [18565] 0.163 0.164 0.331 0.164 0.163 0.164 0.163 0.164 0.175 0.268 0.163 0.163
## [18577] 0.163 0.189 0.164 0.192 0.163 0.164 0.169 0.327 0.186 0.174 0.164 0.174
## [18589] 0.332 0.164 0.164 0.198 0.164 0.176 0.255 0.381 0.164 0.164 0.164 0.163
## [18601] 0.164 0.164 0.164 0.163 0.230 0.164 0.163 0.164 0.164 0.164 0.163 0.258
## [18613] 0.164 0.173 0.174 0.331 0.173 0.164 0.163 0.164 0.164 0.164 0.164 0.164
## [18625] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.329 0.164 0.164 0.164
## [18637] 0.164 0.188 0.211 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18649] 0.173 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18661] 0.183 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18673] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18685] 0.164 0.164 0.201 0.164 0.179 0.164 0.164 0.164 0.164 0.163 0.164 0.164
## [18697] 0.164 0.164 0.164 0.164 0.186 0.164 0.164 0.164 0.164 0.164 0.163 0.164
## [18709] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.163 0.164 0.164
## [18721] 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164
## [18733] 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.163
## [18745] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164
## [18757] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163
## [18769] 0.164 0.164 0.164 0.172 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.171
## [18781] 0.164 0.164 0.164 0.384 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.164
## [18793] 0.164 0.163 0.164 0.233 0.163 0.164 0.164 0.163 0.164 0.164 0.163 0.164
## [18805] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18817] 0.329 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18829] 0.164 0.164 0.164 0.164 0.326 0.164 0.164 0.250 0.164 0.164 0.249 0.164
## [18841] 0.164 0.399 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18853] 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.294 0.164 0.164
## [18865] 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.163
## [18877] 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163
## [18889] 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163
## [18901] 0.249 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163
## [18913] 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163
## [18925] 0.164 0.164 0.164 0.222 0.332 0.164 0.164 0.163 0.163 0.164 0.164 0.163
## [18937] 0.163 0.164 0.164 0.163 0.188 0.164 0.164 0.225 0.164 0.164 0.164 0.164
## [18949] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.288 0.164 0.164 0.164 0.163
## [18961] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163
## [18973] 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163
## [18985] 0.164 0.183 0.164 0.163 0.164 0.174 0.164 0.163 0.164 0.164 0.164 0.164
## [18997] 0.164 0.176 0.164 0.164 0.164 0.332 0.164 0.164 0.164 0.164 0.164 0.324
## [19009] 0.164 0.164 0.164 0.164 0.164 0.164 0.186 0.164 0.164 0.164 0.164 0.211
## [19021] 0.163 0.164 0.164 0.164 0.164 0.332 0.164 0.254 0.168 0.164 0.164 0.164
## [19033] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [19045] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [19057] 0.164 0.171 0.164 0.164 0.164 0.211 0.164 0.164 0.164 0.309 0.164 0.164
## [19069] 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.164 0.163 0.164 0.164
## [19081] 0.163 0.163 0.164 0.164 0.164 0.163 0.164 0.165 0.164 0.164 0.164 0.164
## [19093] 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.165 0.163 0.164 0.164
## [19105] 0.163 0.164 0.164 0.163 0.164 0.164 0.163 0.164 0.164 0.164 0.172 0.164
## [19117] 0.164 0.164 0.171 0.164 0.164 0.164 0.164 0.164 0.175 0.164 0.164 0.433
## [19129] 0.326 0.164 0.227 0.324 0.164 0.341 0.230 0.164 0.165 0.282 0.315 0.164
## [19141] 0.189 0.326 0.164 0.436 0.164 0.164 0.328 0.164 0.164 0.326 0.311 0.164
## [19153] 0.164 0.263 0.164 0.328 0.270 0.321 0.164 0.278 0.164 0.251 0.164 0.185
## [19165] 0.352 0.164 0.164 0.298 0.164 0.163 0.267 0.385 0.298 0.323 0.227 0.332
## [19177] 0.164 0.313 0.327 0.164 0.164 0.323 0.323 0.164 0.406 0.348 0.164 0.164
## [19189] 0.164 0.164 0.164 0.164 0.164 0.164 0.326 0.164 0.164 0.164 0.164 0.164
## [19201] 0.164 0.227 0.164 0.164 0.194 0.164 0.228 0.169 0.164 0.331 0.182 0.164
## [19213] 0.169 0.165 0.179 0.164 0.164 0.169 0.164 0.164 0.165 0.341 0.164 0.164
## [19225] 0.165 0.163 0.164 0.164 0.195 0.164 0.164 0.164 0.208 0.164 0.164 0.163
## [19237] 0.163 0.168 0.164 0.163 0.328 0.164 0.164 0.175 0.216 0.164 0.164 0.164
## [19249] 0.210 0.164 0.311 0.164 0.252 0.164 0.266 0.164 0.174 0.291 0.200 0.164
## [19261] 0.170 0.163 0.163 0.238 0.164 0.163 0.191 0.165 0.217 0.163 0.164 0.164
## [19273] 0.179 0.169 0.298 0.329 0.220 0.164 0.248 0.164 0.178 0.164 0.163 0.164
## [19285] 0.327 0.164 0.245 0.164 0.289 0.329 0.164 0.164 0.323 0.163 0.176 0.332
## [19297] 0.163 0.163 0.330 0.316 0.482 0.331 0.306 0.239 0.164 0.164 0.163 0.164
## [19309] 0.190 0.163 0.224 0.164 0.164 0.184 0.310 0.164 0.164 0.180 0.164 0.310
## [19321] 0.316 0.325 0.304 0.164 0.164 0.216 0.164 0.252 0.211 0.164 0.268 0.318
## [19333] 0.275 0.164 0.163 0.174 0.195 0.164 0.261 0.164 0.242 0.164 0.176 0.164
## [19345] 0.163 0.164 0.168 0.164 0.163 0.164 0.180 0.164 0.222 0.164 0.190 0.311
## [19357] 0.222 0.316 0.240 0.332 0.163 0.328 0.323 0.196 0.244 0.247 0.340 0.163
## [19369] 0.163 0.225 0.331 0.211 0.240 0.164 0.331 0.239 0.235 0.260 0.265 0.165
## [19381] 0.163 0.244 0.253 0.164 0.267 0.287 0.165 0.164 0.164 0.200 0.165 0.278
## [19393] 0.164 0.165 0.237 0.164 0.164 0.165 0.164 0.163 0.164 0.163 0.164 0.260
## [19405] 0.164 0.164 0.164 0.254 0.164 0.163 0.164 0.239 0.199 0.164 0.163 0.164
## [19417] 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.174
## [19429] 0.163 0.163 0.164 0.304 0.163 0.163 0.163 0.164 0.164 0.163 0.163 0.163
## [19441] 0.163 0.163 0.163 0.164 0.164 0.333 0.309 0.164 0.164 0.206 0.217 0.203
## [19453] 0.246 0.245 0.164 0.323 0.164 0.167 0.164 0.163 0.168 0.163 0.164 0.163
## [19465] 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163
## [19477] 0.164 0.163 0.164 0.163 0.164 0.163 0.163 0.164 0.163 0.164 0.163 0.164
## [19489] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.163 0.164
## [19501] 0.164 0.165 0.186 0.164 0.164 0.165 0.163 0.164 0.164 0.164 0.163 0.164
## [19513] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164
## [19525] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.328 0.164 0.290 0.164
## [19537] 0.328 0.164 0.164 0.164 0.164 0.164 0.164 0.175 0.164 0.164 0.167 0.222
## [19549] 0.284 0.165 0.279 0.164 0.215 0.317 0.164 0.230 0.204 0.190 0.296 0.312
## [19561] 0.215 0.295 0.268 0.164 0.163 0.332 0.315 0.317 0.163 0.164 0.278 0.211
## [19573] 0.169 0.164 0.163 0.222 0.163 0.164 0.163 0.164 0.328 0.164 0.163 0.278
## [19585] 0.253 0.164 0.163 0.164 0.166 0.164 0.183 0.163 0.163 0.164 0.278 0.331
## [19597] 0.164 0.267 0.164 0.164 0.164 0.251 0.164 0.323 0.164 0.164 0.237 0.164
## [19609] 0.164 0.164 0.164 0.164 0.183 0.164 0.164 0.302 0.164 0.330 0.172 0.164
## [19621] 0.164 0.229 0.300 0.164 0.164 0.235 0.164 0.164 0.164 0.193 0.164 0.222
## [19633] 0.286 0.164 0.164 0.163 0.252 0.236 0.164 0.163 0.164 0.323 0.163 0.163
## [19645] 0.164 0.190 0.398 0.251 0.165 0.164 0.164 0.181 0.164 0.255 0.164 0.163
## [19657] 0.165 0.165 0.164 0.164 0.165 0.181 0.164 0.164 0.163 0.174 0.164 0.164
## [19669] 0.178 0.164 0.164 0.170 0.164 0.164 0.163 0.185 0.164 0.165 0.163 0.164
## [19681] 0.164 0.165 0.163 0.164 0.164 0.165 0.163 0.164 0.164 0.165 0.163 0.164
## [19693] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164
## [19705] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.176
## [19717] 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164
## [19729] 0.164 0.164 0.163 0.164 0.164 0.165 0.163 0.260 0.164 0.164 0.163 0.164
## [19741] 0.214 0.164 0.164 0.164 0.170 0.164 0.164 0.164 0.164 0.292 0.164 0.263
## [19753] 0.164 0.164 0.188 0.211 0.319 0.332 0.284 0.164 0.190 0.163 0.200 0.218
## [19765] 0.205 0.238 0.164 0.163 0.226 0.185 0.177 0.234 0.185 0.164 0.253 0.164
## [19777] 0.215 0.278 0.164 0.176 0.222 0.164 0.164 0.164 0.237 0.164 0.164 0.164
## [19789] 0.164 0.164 0.164 0.164 0.307 0.164 0.164 0.254 0.164 0.164 0.164 0.164
## [19801] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.238 0.164 0.164
## [19813] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [19825] 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.165
## [19837] 0.164 0.164 0.165 0.163 0.164 0.164 0.165 0.164 0.164 0.165 0.163 0.164
## [19849] 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.304 0.163 0.164 0.164
## [19861] 0.222 0.164 0.164 0.360 0.164 0.164 0.165 0.170 0.164 0.164 0.165 0.243
## [19873] 0.164 0.164 0.165 0.163 0.164 0.164 0.165 0.163 0.164 0.164 0.179 0.163
## [19885] 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163
## [19897] 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163
## [19909] 0.164 0.164 0.163 0.181 0.164 0.164 0.310 0.202 0.164 0.164 0.318 0.164
## [19921] 0.279 0.164 0.165 0.228 0.163 0.164 0.164 0.181 0.205 0.164 0.169 0.168
## [19933] 0.164 0.193 0.163 0.179 0.209 0.163 0.163 0.163 0.163 0.163 0.163 0.163
## [19945] 0.163 0.163 0.163 0.163 0.163 0.163 0.225 0.188 0.163 0.163 0.163 0.163
## [19957] 0.163 0.163 0.163 0.224 0.164 0.163 0.196 0.164 0.163 0.163 0.173 0.166
## [19969] 0.163 0.163 0.164 0.163 0.163 0.163 0.331 0.164 0.163 0.163 0.163 0.164
## [19981] 0.163 0.163 0.237 0.163 0.164 0.163 0.163 0.164 0.163 0.163 0.163 0.163
## [19993] 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.189 0.164 0.163 0.163
## [20005] 0.207 0.164 0.163 0.163 0.212 0.163 0.163 0.163 0.164 0.263 0.309 0.164
## [20017] 0.231 0.163 0.189 0.329 0.259 0.163 0.164 0.328 0.238 0.328 0.164 0.270
## [20029] 0.164 0.252 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164
## [20041] 0.164 0.165 0.163 0.164 0.164 0.165 0.211 0.164 0.164 0.165 0.165 0.165
## [20053] 0.165 0.165 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20065] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.181 0.166 0.193
## [20077] 0.165 0.164 0.165 0.166 0.164 0.165 0.168 0.165 0.165 0.169 0.165 0.168
## [20089] 0.165 0.171 0.165 0.179 0.179 0.175 0.165 0.168 0.165 0.173 0.178 0.164
## [20101] 0.173 0.164 0.178 0.164 0.176 0.164 0.171 0.164 0.168 0.164 0.169 0.164
## [20113] 0.169 0.164 0.170 0.164 0.172 0.164 0.171 0.164 0.173 0.164 0.170 0.164
## [20125] 0.170 0.164 0.166 0.164 0.164 0.164 0.164 0.164 0.164 0.181 0.164 0.166
## [20137] 0.164 0.165 0.164 0.169 0.168 0.168 0.169 0.166 0.168 0.166 0.171 0.167
## [20149] 0.168 0.165 0.168 0.165 0.168 0.165 0.168 0.165 0.168 0.165 0.167 0.165
## [20161] 0.166 0.167 0.167 0.165 0.166 0.166 0.171 0.167 0.168 0.165 0.167 0.165
## [20173] 0.167 0.165 0.168 0.165 0.171 0.171 0.164 0.164 0.164 0.164 0.165 0.164
## [20185] 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.163 0.165
## [20197] 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.165 0.164
## [20209] 0.165 0.164 0.216 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20221] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20233] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20245] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20257] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20269] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20281] 0.165 0.165 0.167 0.165 0.165 0.165 0.165 0.167 0.165 0.165 0.165 0.165
## [20293] 0.165 0.187 0.165 0.165 0.177 0.165 0.172 0.165 0.166 0.165 0.165 0.164
## [20305] 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20317] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20329] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.165 0.165
## [20341] 0.165 0.165 0.165 0.165 0.165 0.165 0.170 0.184 0.165 0.180 0.176 0.165
## [20353] 0.178 0.191 0.165 0.190 0.176 0.165 0.174 0.164 0.165 0.177 0.170 0.165
## [20365] 0.181 0.165 0.165 0.179 0.165 0.177 0.165 0.176 0.165 0.169 0.165 0.168
## [20377] 0.165 0.176 0.164 0.176 0.164 0.176 0.164 0.177 0.164 0.172 0.164 0.170
## [20389] 0.164 0.172 0.164 0.173 0.164 0.171 0.164 0.174 0.164 0.173 0.164 0.170
## [20401] 0.164 0.171 0.164 0.171 0.164 0.172 0.164 0.164 0.164 0.164 0.164 0.164
## [20413] 0.187 0.164 0.172 0.164 0.167 0.164 0.165 0.164 0.169 0.164 0.166 0.164
## [20425] 0.167 0.164 0.172 0.164 0.168 0.173 0.164 0.166 0.164 0.168 0.164 0.173
## [20437] 0.173 0.167 0.164 0.170 0.164 0.170 0.168 0.164 0.167 0.164 0.169 0.164
## [20449] 0.171 0.180 0.164 0.164 0.165 0.165 0.165 0.164 0.165 0.165 0.164 0.165
## [20461] 0.164 0.165 0.165 0.164 0.164 0.165 0.165 0.164 0.165 0.164 0.165 0.165
## [20473] 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165 0.165 0.164 0.165
## [20485] 0.165 0.164 0.165 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164
## [20497] 0.164 0.164 0.164 0.164 0.165 0.164 0.165 0.164 0.164 0.164 0.164 0.164
## [20509] 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.165 0.164
## [20521] 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.168 0.164 0.164
## [20533] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20545] 0.164 0.164 0.164 0.165 0.165 0.165 0.165 0.164 0.164 0.165 0.165 0.164
## [20557] 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20569] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20581] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.164
## [20593] 0.164 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [20605] 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20617] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20629] 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165
## [20641] 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.165 0.165 0.164 0.164 0.165
## [20653] 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165 0.165 0.164 0.164 0.164
## [20665] 0.164 0.162 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20677] 0.164 0.164 0.165 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164
## [20689] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20701] 0.176 0.186 0.195 0.191 0.199 0.184 0.209 0.239 0.330 0.183 0.208 0.205
## [20713] 0.164 0.194 0.164 0.183 0.164 0.175 0.204 0.190 0.242 0.304 0.276 0.231
## [20725] 0.167 0.167 0.168 0.168 0.168 0.170 0.164 0.253 0.215 0.242 0.245 0.223
## [20737] 0.195 0.197 0.221 0.227 0.189 0.242 0.164 0.226 0.189 0.172 0.192 0.208
## [20749] 0.168 0.192 0.208 0.168 0.234 0.192 0.177 0.261 0.236 0.213 0.165 0.164
## [20761] 0.164 0.165 0.164 0.164 0.164 0.165 0.242 0.279 0.187 0.271 0.208 0.172
## [20773] 0.165 0.165 0.164 0.164 0.165 0.165 0.163 0.165 0.165 0.504 0.220 0.165
## [20785] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.164 0.164 0.164
## [20797] 0.163 0.164 0.164 0.222 0.303 0.346 0.165 0.164 0.165 0.166 0.164 0.164
## [20809] 0.164 0.164 0.164 0.163 0.163 0.163 0.164 0.164 0.164 0.164 0.164 0.164
## [20821] 0.164 0.164 0.164 0.163 0.163 0.163 0.185 0.163 0.163 0.179 0.179 0.178
## [20833] 0.178 0.171 0.166 0.163 0.163 0.165 0.163 0.345 0.164 0.164 0.164 0.164
## [20845] 0.225 0.412 0.165 0.214 0.240 0.165 0.165 0.236 0.165 0.172 0.177 0.216
## [20857] 0.188 0.166 0.195 0.165 0.239 0.305 0.258 0.230 0.196 0.575 0.199 0.182
## [20869] 0.235 0.164 0.295 0.164 0.165 0.214 0.208 0.165 0.164 0.164 0.164 0.164
## [20881] 0.164 0.164 0.164 0.162 0.162 0.162 0.162 0.162 0.164 0.183 0.181 0.173
## [20893] 0.172 0.174 0.218 0.388 0.164 0.236 0.264 0.189 0.194 0.164 0.164 0.164
## [20905] 0.164 0.164 0.164 0.164 0.164 0.177 0.297 0.235 0.195 0.171 0.164 0.164
## [20917] 0.165 0.165 0.166 0.164 0.165 0.165 0.165 0.165 0.165 0.164 0.165 0.164
## [20929] 0.164 0.165 0.165 0.165 0.202 0.164 0.165 0.164 0.165 0.165 0.165 0.165
## [20941] 0.165 0.165 0.165 0.162 0.162 0.193 0.179 0.178 0.170 0.164 0.164 0.165
## [20953] 0.165 0.164 0.165 0.201 0.314 0.339 0.233 0.320 0.524 0.164 0.217 0.174
## [20965] 0.164 0.164 0.423 0.214 0.226 0.200 0.198 0.167 0.186 0.314 0.211 0.164
## [20977] 0.319 0.328 0.164 0.192 0.197 0.164 0.164 0.222 0.326 0.211 0.165 0.203
## [20989] 0.179 0.193 0.218 0.225 0.227 0.205 0.169 0.260 0.199 0.164 0.303 0.311
## [21001] 0.193 0.298 0.221 0.278 0.171 0.191 0.385 0.320 0.344 0.225 0.268 0.257
## [21013] 0.300 0.344 0.332 0.357 0.323 0.227 0.236 0.233 0.258 0.241 0.231 0.263
## [21025] 0.262 0.276 0.273 0.269 0.272 0.273 0.278 0.270 0.275 0.251 0.219 0.274
## [21037] 0.276 0.275 0.287 0.284 0.286 0.362 0.277 0.275 0.272 0.181 0.178 0.275
## [21049] 0.261 0.264 0.256 0.261 0.229 0.229 0.224 0.255 0.171 0.177 0.180 0.181
## [21061] 0.192 0.168 0.181 0.189 0.169 0.181 0.170 0.175 0.182 0.178 0.180 0.186
## [21073] 0.175 0.172 0.167 0.169 0.165 0.164 0.167 0.168 0.165 0.165 0.170 0.166
## [21085] 0.165 0.167 0.172 0.165 0.177 0.174 0.183 0.200 0.185 0.169 0.179 0.166
## [21097] 0.174 0.167 0.174 0.170 0.164 0.193 0.200 0.177 0.175 0.177 0.183 0.177
## [21109] 0.187 0.176 0.180 0.181 0.173 0.168 0.176 0.173 0.166 0.168 0.168 0.165
## [21121] 0.167 0.168 0.164 0.162 0.164 0.167 0.164 0.167 0.164 0.166 0.166 0.166
## [21133] 0.166 0.172 0.166 0.164 0.166 0.164 0.170 0.178 0.164 0.167 0.166 0.176
## [21145] 0.164 0.165 0.168 0.166 0.164 0.168 0.164 0.166 0.164 0.164 0.164 0.166
## [21157] 0.164 0.164 0.164 0.166 0.168 0.164 0.167 0.168 0.169 0.167 0.199 0.170
## [21169] 0.166 0.170 0.174 0.166 0.206 0.165 0.177 0.167 0.164 0.164 0.163 0.163
## [21181] 0.164 0.321 0.273 0.272 0.270 0.270 0.262 0.273 0.270 0.250 0.272 0.269
## [21193] 0.207 0.268 0.271 0.271 0.251 0.233 0.273 0.269 0.269 0.247 0.266 0.271
## [21205] 0.264 0.294 0.225 0.242 0.229 0.221 0.274 0.215 0.263 0.224 0.186 0.174
## [21217] 0.170 0.264 0.301 0.241 0.230 0.265 0.238 0.281 0.280 0.176 0.176 0.282
## [21229] 0.270 0.268 0.257 0.266 0.258 0.271 0.270 0.271 0.172 0.165 0.164 0.164
## [21241] 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.167 0.168 0.164 0.164 0.169
## [21253] 0.164 0.168 0.167 0.168 0.164 0.168 0.164 0.164 0.164 0.164 0.164 0.164
## [21265] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.167 0.164 0.173 0.175
## [21277] 0.167 0.169 0.165 0.181 0.182 0.162 0.168 0.162 0.163 0.162 0.179 0.163
## [21289] 0.170 0.162 0.168 0.167 0.167 0.170 0.167 0.166 0.168 0.165 0.166 0.165
## [21301] 0.165 0.165 0.164 0.177 0.167 0.169 0.165 0.168 0.169 0.162 0.167 0.165
## [21313] 0.165 0.172 0.165 0.180 0.188 0.171 0.173 0.164 0.165 0.162 0.162 0.162
## [21325] 0.162 0.172 0.204 0.164 0.339 0.239 0.196 0.165 0.179 0.168 0.169 0.177
## [21337] 0.192 0.176 0.165 0.164 0.165 0.165 0.196 0.162 0.162 0.238 0.273 0.163
## [21349] 0.165 0.258 0.163 0.165 0.265 0.163 0.163 0.299 0.163 0.236 0.190 0.254
## [21361] 0.165 0.291 0.233 0.242 0.243 0.306 0.228 0.163 0.247 0.252 0.231 0.277
## [21373] 0.266 0.257 0.280 0.290 0.164 0.291 0.267 0.164 0.287 0.164 0.164 0.164
## [21385] 0.215 0.164 0.205 0.209 0.259 0.220 0.314 0.285 0.222 0.182 0.328 0.219
## [21397] 0.240 0.369 0.330 0.349 0.181 0.165 0.202 0.163 0.186 0.163 0.182 0.163
## [21409] 0.163 0.165 0.176 0.163 0.163 0.165 0.177 0.163 0.163 0.165 0.180 0.163
## [21421] 0.165 0.162 0.164 0.165 0.165 0.165 0.164 0.164 0.165 0.162 0.173 0.165
## [21433] 0.165 0.162 0.164 0.164 0.165 0.162 0.164 0.164 0.165 0.164 0.164 0.164
## [21445] 0.165 0.162 0.164 0.164 0.165 0.173 0.164 0.164 0.165 0.162 0.164 0.164
## [21457] 0.165 0.165 0.164 0.164 0.165 0.165 0.164 0.164 0.165 0.165 0.164 0.164
## [21469] 0.165 0.164 0.164 0.164 0.165 0.165 0.164 0.164 0.165 0.165 0.164 0.164
## [21481] 0.165 0.197 0.164 0.164 0.165 0.170 0.164 0.164 0.165 0.165 0.164 0.164
## [21493] 0.165 0.165 0.164 0.164 0.165 0.230 0.164 0.164 0.165 0.181 0.164 0.164
## [21505] 0.165 0.163 0.164 0.164 0.169 0.162 0.164 0.164 0.165 0.165 0.164 0.223
## [21517] 0.166 0.165 0.164 0.164 0.165 0.165 0.164 0.174 0.165 0.165 0.164 0.183
## [21529] 0.165 0.165 0.164 0.168 0.167 0.190 0.165 0.164 0.164 0.165 0.172 0.165
## [21541] 0.183 0.164 0.165 0.172 0.165 0.229 0.164 0.165 0.170 0.165 0.222 0.164
## [21553] 0.165 0.178 0.165 0.261 0.165 0.165 0.168 0.165 0.221 0.164 0.171 0.165
## [21565] 0.220 0.164 0.171 0.165 0.174 0.165 0.171 0.165 0.178 0.165 0.171 0.165
## [21577] 0.168 0.165 0.165 0.170 0.165 0.164 0.165 0.165 0.163 0.165 0.164 0.165
## [21589] 0.162 0.165 0.166 0.165 0.162 0.167 0.164 0.165 0.167 0.164 0.165 0.165
## [21601] 0.164 0.165 0.167 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.165 0.165
## [21613] 0.164 0.165 0.165 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.165 0.164
## [21625] 0.164 0.165 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.165 0.164
## [21637] 0.164 0.165 0.165 0.224 0.164 0.207 0.164 0.164 0.178 0.164 0.163 0.222
## [21649] 0.164 0.164 0.170 0.164 0.164 0.229 0.164 0.164 0.234 0.164 0.164 0.219
## [21661] 0.164 0.164 0.172 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165
## [21673] 0.164 0.181 0.181 0.164 0.164 0.222 0.164 0.164 0.220 0.164 0.164 0.188
## [21685] 0.171 0.164 0.165 0.164 0.164 0.249 0.164 0.164 0.191 0.164 0.164 0.178
## [21697] 0.170 0.164 0.199 0.169 0.164 0.195 0.164 0.164 0.252 0.164 0.169 0.257
## [21709] 0.282 0.164 0.206 0.164 0.164 0.201 0.164 0.164 0.176 0.164 0.164 0.213
## [21721] 0.164 0.164 0.176 0.164 0.164 0.172 0.164 0.164 0.162 0.164 0.164 0.173
## [21733] 0.164 0.164 0.162 0.168 0.162 0.164 0.164 0.162 0.164 0.164 0.162 0.164
## [21745] 0.162 0.164 0.162 0.162 0.164 0.229 0.162 0.164 0.216 0.162 0.164 0.258
## [21757] 0.165 0.164 0.166 0.175 0.189 0.254 0.165 0.191 0.164 0.165 0.175 0.164
## [21769] 0.229 0.164 0.179 0.164 0.164 0.179 0.164 0.164 0.164 0.164 0.174 0.164
## [21781] 0.164 0.171 0.164 0.164 0.325 0.164 0.164 0.165 0.165 0.163 0.163 0.163
## [21793] 0.165 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.164
## [21805] 0.163 0.163 0.165 0.168 0.165 0.163 0.165 0.165 0.163 0.164 0.163 0.162
## [21817] 0.165 0.166 0.166 0.166 0.166 0.166 0.167 0.169 0.166 0.168 0.166 0.166
## [21829] 0.167 0.166 0.165 0.166 0.166 0.168 0.166 0.166 0.167 0.167 0.167 0.164
## [21841] 0.206 0.229 0.165 0.165 0.166 0.166 0.165 0.166 0.176 0.167 0.167 0.167
## [21853] 0.165 0.166 0.166 0.166 0.167 0.167 0.167 0.168 0.167 0.168 0.167 0.170
## [21865] 0.169 0.170 0.168 0.170 0.170 0.172 0.174 0.170 0.167 0.165 0.165 0.165
## [21877] 0.165 0.166 0.166 0.165 0.169 0.194 0.210 0.211 0.193 0.190 0.189 0.174
## [21889] 0.188 0.174 0.197 0.184 0.186 0.185 0.179 0.182 0.185 0.173 0.178 0.187
## [21901] 0.191 0.177 0.183 0.183 0.174 0.176 0.174 0.174 0.178 0.186 0.185 0.172
## [21913] 0.172 0.183 1.109 0.177 0.345 0.654 0.564 0.388 0.204 0.182 0.478 0.676
## [21925] 0.315 0.641 0.254 0.746 0.203 0.313 0.862 2.086 1.283 1.586 1.243 0.911
## [21937] 0.212 0.320 1.584 1.096 0.911 1.493 0.520 0.267 0.537 0.576 0.453 0.830
## [21949] 0.202 0.172 0.182 0.223 0.226 0.163 0.315 0.557 0.286 0.191 0.214 0.361
## [21961] 0.489 0.200 0.289 0.424 0.362 0.523 0.545 0.315 0.440 0.452 0.585 0.461
## [21973] 0.545 0.209 0.296 0.598 0.560 0.185 0.181 0.165 0.163 0.163 0.163 0.165
## [21985] 0.167 0.334 0.178 0.330 0.271 0.428 0.337 0.426 0.276 0.260 0.321 0.272
## [21997] 0.164 0.329 0.164 0.629 0.293 0.397 0.328 0.321 0.430 0.489 0.333 0.578
## [22009] 0.261 0.405 0.518 0.404 0.388 0.266 0.492 0.309 0.565 0.439 0.312 0.462
## [22021] 0.303 0.467 0.368 0.579 0.745 0.721 0.644 0.460 0.213 0.365 0.337 0.223
## [22033] 0.237 0.266 0.304 0.347 0.456 2.034 0.174 0.381 0.211 0.424 0.164 0.165
## [22045] 0.166 0.165 0.165 0.216 0.165 0.169 0.164 0.166 0.164 0.173 0.165 0.188
## [22057] 0.164 0.168 0.170 0.176 0.171 0.173 0.167 0.170 0.165 0.168 0.165 0.166
## [22069] 0.165 0.166 0.164 0.164 0.163 0.165 0.164 0.165 0.164 0.166 0.164 0.167
## [22081] 0.163 0.166 0.200 0.166 0.166 0.168 0.170 0.169 0.167 0.167 0.170 0.169
## [22093] 0.164 0.166 0.166 0.167 0.166 0.265 0.165 0.165 0.164 0.164 0.164 0.165
## [22105] 0.165 0.165 0.209 0.173 0.176 0.169 0.165 0.166 0.164 0.165 0.163 0.435
## [22117] 0.413 0.310 0.211 0.168 0.191 0.324 0.246 0.272 0.682 3.278 0.164 0.162
## [22129] 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.164
## [22141] 0.164 0.164 0.164 0.165 0.165 0.164 0.164 0.164 0.164 0.165 0.164 0.165
## [22153] 0.163 0.165 0.164 0.164 0.162 0.162 0.163 0.164 0.163 0.165 0.164 0.164
## [22165] 0.164 0.165 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.166
## [22177] 0.164 0.165 0.164 0.165 0.165 0.164 0.165 0.164 0.164 0.165 0.164 0.179
## [22189] 0.164 0.164 0.162 0.163 0.165 0.165 0.164 0.164 0.164 0.163 0.165 0.164
## [22201] 0.164 0.164 0.165 0.165 0.164 0.164 0.164 0.165 0.164 0.164 0.163 0.164
## [22213] 0.164 0.185 0.202 0.280 0.172 0.197 0.175 0.185 0.174 0.200 0.224 0.236
## [22225] 0.179 0.227 0.258 0.271 0.164 0.283 0.214 0.340 0.183 0.173 0.165 0.355
## [22237] 0.324 0.249 0.245 0.219 0.177 0.269 0.192 0.211 0.277 0.247 0.273 0.300
## [22249] 0.275 0.344 0.334 0.373 0.279 0.250 0.189 0.251 0.181 0.207 0.167 0.176
## [22261] 0.176 0.170 0.367 0.181 0.365 0.177 0.261 0.231 0.264 0.245 0.245 0.281
## [22273] 0.237 0.218 0.207 0.182 0.190 0.190 0.275 0.213 0.242 0.277 0.205 0.331
## [22285] 0.238 0.230 0.327 0.208 0.266 0.200 0.227 0.179 0.234 0.193 0.187 0.231
## [22297] 0.209 0.820 0.477 0.638 0.375 0.164 0.378 0.454 0.365 0.414 0.361 0.254
## [22309] 0.398 0.331 0.425 0.300 0.597 0.260 0.373 0.277 0.352 0.253 0.348 0.520
## [22321] 0.368 0.317 0.366 0.196 0.362 0.244 0.253 0.247 0.347 0.325 0.402 0.249
## [22333] 0.586 0.296 0.499 0.359 0.486 0.327 0.403 0.502 0.227 0.367 0.261 0.394
## [22345] 0.391 0.430 0.341 0.382 0.338 0.437 0.321 0.442 0.358 0.404 0.359 0.393
## [22357] 0.326 0.358 0.385 0.428 0.253 0.280 0.431 0.505 0.583 0.726 0.620 0.733
## [22369] 0.723 0.680 0.425 0.877 0.193 0.473 0.359 0.297 0.851 0.319 0.327 0.241
## [22381] 0.322 0.323 0.357 0.408 0.396 0.498 0.444 0.465 0.166 0.168 0.171 0.166
## [22393] 0.165 0.179 0.166 0.171 0.170 0.176 0.252 0.222 0.177 0.170 0.166 0.175
## [22405] 0.171 0.166 0.388 0.165 0.170 0.163 0.505 0.165 0.165 0.164 0.208 0.164
## [22417] 0.164 0.165 0.191 0.204 0.193 0.203 0.215 0.182 0.199 0.164 0.164 0.204
## [22429] 0.192 0.205 0.204 0.222 0.224 0.247 0.196 0.228 0.205 0.182 0.173 0.165
## [22441] 0.167 0.219 0.442 0.264 0.187 0.229 0.162 0.177 0.164 0.165 0.164 0.730
## [22453] 0.816 0.490 0.350 0.349 0.559 0.248 0.584 0.390 0.267 0.230 0.300 0.210
## [22465] 0.426 0.494 0.288 0.344 0.287 0.212 0.260 0.498 0.357 0.360 0.232 0.256
## [22477] 0.413 0.233 0.605 0.200 0.181 0.718 0.302 0.512 0.380 0.369 0.580 0.317
## [22489] 0.367 0.353 0.380 0.455 0.415 0.327 0.333 0.283 0.761 0.357 0.338 0.304
## [22501] 0.164 0.164 0.165 0.232 0.248 0.402 0.296 0.192 0.211 0.261 0.278 0.306
## [22513] 0.249 0.190 0.199 0.198 0.310 0.261 0.186 0.375 0.294 0.250 0.252 0.338
## [22525] 1.305 0.189 0.185 0.352 0.466 0.644 0.735 0.840 0.252 0.243 0.189 0.265
## [22537] 0.210 0.243 0.203 0.224 0.327 0.329 0.234 0.305 0.196 0.631 0.521 0.376
## [22549] 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.321 0.256 0.313 0.236
## [22561] 0.288 0.193 0.164 0.165 0.336 0.274 0.425 0.285 0.291 0.237 0.208 0.188
## [22573] 0.217 0.231 0.270 0.248 0.215 0.284 0.247 0.165 0.450 0.230 0.307 0.164
## [22585] 0.272 0.187 0.457 0.422 0.269 0.358 0.263 0.212 0.244 0.164 0.197 0.211
## [22597] 0.242 0.165 0.291 0.176 0.228 0.178 0.287 0.170 0.359 0.165 0.326 0.206
## [22609] 0.364 0.357 0.186 0.286 0.350 0.204 0.228 0.200 0.243 0.174 0.185 0.169
## [22621] 0.166 0.181 0.191 0.254 0.219 0.398 0.215 0.401 0.195 0.342 0.198 0.366
## [22633] 0.194 0.300 0.204 0.278 0.226 0.254 0.195 0.490 0.247 0.271 0.510 0.603
## [22645] 0.457 0.476 0.359 0.376 0.452 0.398 0.254 0.353 0.223 0.328 0.217 0.312
## [22657] 0.223 0.238 0.335 0.236 0.170 0.243 0.206 0.249 0.190 0.241 0.261 0.286
## [22669] 0.199 0.171 0.291 0.317 0.186 0.304 0.382 0.253 0.189 0.191 0.246 0.540
## [22681] 0.264 0.162 0.183 0.260 0.258 0.278 0.175 0.331 1.085 0.164 0.164 0.165
## [22693] 0.164 0.164 0.165 0.164 0.164 0.165 0.165 0.164 0.165 0.165 0.164 0.164
## [22705] 0.164 0.164 0.164 0.164 0.164 0.327 0.650 0.164 0.163 0.164 0.165 0.163
## [22717] 0.162 0.163 0.164 0.164 0.165 0.164 0.164 0.212 0.165 0.166 0.170 0.183
## [22729] 0.399 0.475 0.320 0.378 0.408 0.368 0.352 0.493 0.323 0.228 0.470 0.360
## [22741] 0.404 0.598 0.470 0.606 0.542 0.695 0.312 0.461 0.199 0.569 0.231 0.471
## [22753] 0.755 0.611 0.460 0.386 0.274 0.372 0.450 0.419 0.378 0.465 0.520 0.391
## [22765] 0.523 0.655 0.501 0.319 0.339 0.422 0.435 0.396 0.337 0.352 0.346 0.401
## [22777] 0.435 0.340 0.219 0.547 0.264 0.466 0.645 0.438 0.222 0.454 0.265 0.427
## [22789] 0.346 0.383 0.410 0.406 0.465 0.389 0.502 0.394 0.430 0.326 0.387 0.435
## [22801] 0.351 0.385 0.360 0.398 0.484 0.569 0.397 0.412 0.381 0.344 0.566 0.401
## [22813] 0.382 0.568 0.560 0.519 0.530 0.616 0.526 0.560 0.404 0.218 0.570 0.640
## [22825] 0.436 0.558 0.630 0.530 0.598 0.556 0.554 0.271 0.188 0.203 0.206 0.244
## [22837] 0.225 0.240 0.172 0.178 0.402 0.450 0.465 0.513 0.433 0.407 0.326 0.350
## [22849] 0.323 0.336 0.491 0.473 0.356 0.399 0.230 0.193 0.225 0.267 0.217 0.229
## [22861] 0.252 0.194 0.307 0.255 0.219 0.229 0.234 0.223 0.241 0.212 0.285 0.242
## [22873] 0.281 0.241 0.252 0.216 0.279 0.199 0.344 0.242 0.450 0.337 0.261 0.316
## [22885] 0.252 0.353 0.251 0.248 0.295 0.252 0.196 0.219 0.215 0.242 0.222 0.206
## [22897] 0.245 0.246 0.318 0.245 0.253 0.270 0.242 0.239 0.256 0.244 0.222 0.228
## [22909] 0.229 0.249 0.377 0.261 0.379 0.246 0.454 0.219 0.390 0.290 0.406 0.302
## [22921] 0.472 0.313 0.502 0.292 0.362 0.290 0.437 0.292 0.296 0.243 0.272 0.214
## [22933] 0.274 0.241 0.360 0.357 0.357 0.368 0.358 0.354 0.354 0.358 0.519 0.411
## [22945] 0.612 0.410 0.519 0.411 0.276 0.208 0.463 0.221 0.331 0.512 0.586 0.413
## [22957] 0.562 0.559 0.516 0.340 0.514 0.285 0.419 0.431 0.299 0.437 0.446 0.279
## [22969] 0.274 0.347 0.251 0.220 0.299 0.208 0.305 0.272 0.432 0.211 0.338 0.299
## [22981] 0.273 0.216 0.355 0.347 0.268 0.593 0.316 0.409 0.315 0.394 0.165 0.592
## [22993] 0.165 0.408 0.262 0.338 0.180 0.334 0.472 0.382 0.333 0.578 0.360 0.436
## [23005] 0.348 0.333 0.530 0.428 0.372 0.339 0.421 0.331 0.318 0.361 0.327 0.376
## [23017] 0.331 0.491 0.463 0.335 0.284 0.303 0.346 0.369 0.447 0.479 0.349 0.409
## [23029] 0.235 0.392 0.297 0.357 0.412 0.355 0.526 0.436 0.449 0.322 0.384 0.267
## [23041] 0.317 0.237 0.295 0.195 0.260 0.363 0.316 0.195 0.175 0.195 0.183 0.186
## [23053] 0.229 0.211 0.293 0.311 0.247 0.231 0.333 0.164 0.232 0.174 0.230 0.332
## [23065] 0.333 0.185 0.194 0.186 0.163 0.340 0.198 0.203 0.164 0.324 0.305 0.246
## [23077] 0.320 0.164 0.296 0.331 0.352 0.340 0.403 0.321 0.246 0.313 0.277 0.261
## [23089] 0.329 0.320 0.329 0.239 0.403 0.344 0.265 0.305 0.227 0.200 0.332 0.264
## [23101] 0.164 0.164 0.178 0.176 0.164 0.257 0.330 0.310 0.304 0.327 0.164 0.249
## [23113] 0.164 0.163 0.311 0.407 0.239 0.355 0.165 0.211 0.169 0.168 0.180 0.171
## [23125] 0.208 0.173 0.173 0.164 0.164 0.162 0.220 0.248 0.164 0.164 0.163 0.165
## [23137] 0.175 0.164 0.164 0.179 0.164 0.162 0.163 0.164 0.278 0.163 0.163 0.164
## [23149] 0.308 0.163 0.163 0.164 0.165 0.269 0.279 0.164 0.211 0.197 0.282 0.164
## [23161] 0.275 0.165 0.163 0.164 0.165 0.263 0.164 0.165 0.165 0.178 0.164 0.165
## [23173] 0.227 0.255 0.192 0.165 0.165 0.210 0.163 0.165 0.163 0.351 0.165 0.163
## [23185] 0.178 0.250 0.165 0.163 0.164 0.163 0.165 0.163 0.164 0.163 0.185 0.163
## [23197] 0.164 0.178 0.332 0.163 0.164 0.270 0.165 0.163 0.164 0.165 0.165 0.163
## [23209] 0.171 0.216 0.165 0.163 0.164 0.163 0.165 0.163 0.164 0.163 0.165 0.163
## [23221] 0.164 0.163 0.165 0.163 0.164 0.163 0.173 0.163 0.164 0.163 0.163 0.163
## [23233] 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163
## [23245] 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163
## [23257] 0.164 0.163 0.163 0.163 0.164 0.244 0.163 0.164 0.163 0.163 0.369 0.164
## [23269] 0.163 0.163 0.163 0.244 0.163 0.163 0.490 0.165 0.163 0.163 0.195 0.163
## [23281] 0.163 0.164 0.163 0.163 0.163 0.164 0.165 0.163 0.163 0.164 0.163 0.163
## [23293] 0.164 0.163 0.163 0.163 0.213 0.163 0.163 0.163 0.164 0.163 0.163 0.163
## [23305] 0.164 0.165 0.163 0.163 0.164 0.163 0.163 0.163 0.247 0.165 0.163 0.163
## [23317] 0.182 0.165 0.163 0.163 0.164 0.165 0.163 0.164 0.164 0.165 0.184 0.163
## [23329] 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163
## [23341] 0.164 0.194 0.163 0.163 0.164 0.165 0.163 0.164 0.163 0.164 0.163 0.165
## [23353] 0.164 0.163 0.165 0.164 0.163 0.165 0.164 0.165 0.164 0.165 0.164 0.164
## [23365] 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164
## [23377] 0.164 0.164 0.164 0.164 0.163 0.180 0.165 0.163 0.222 0.163 0.182 0.178
## [23389] 0.272 0.163 0.163 0.392 0.163 0.163 0.163 0.163 0.169 0.163 0.163 0.163
## [23401] 0.163 0.164 0.163 0.163 0.163 0.163 0.178 0.164 0.164 0.214 0.163 0.163
## [23413] 0.313 0.323 0.415 0.331 0.312 0.184 0.238 0.164 0.163 0.163 0.163 0.163
## [23425] 0.330 0.186 0.453 0.163 0.163 0.163 0.163 0.164 0.209 0.199 0.163 0.436
## [23437] 0.331 0.164 0.213 0.333 0.490 0.182 0.206 0.211 0.207 0.175 0.164 0.163
## [23449] 0.210 0.164 0.402 0.334 0.323 0.181 0.200 0.163 0.163 0.184 0.248 0.164
## [23461] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.171 0.241 0.164 0.164
## [23473] 0.329 0.231 0.164 0.164 0.181 0.251 0.165 0.164 0.310 0.269 0.164 0.193
## [23485] 0.332 0.164 0.165 0.321 0.184 0.165 0.165 0.317 0.195 0.165 0.165 0.241
## [23497] 0.320 0.165 0.164 0.295 0.322 0.165 0.165 0.177 0.174 0.165 0.165 0.171
## [23509] 0.163 0.165 0.165 0.164 0.169 0.164 0.165 0.167 0.164 0.164 0.165 0.206
## [23521] 0.169 0.164 0.164 0.178 0.296 0.164 0.164 0.164 0.319 0.164 0.165 0.164
## [23533] 0.323 0.165 0.165 0.178 0.332 0.164 0.165 0.191 0.226 0.164 0.165 0.320
## [23545] 0.332 0.164 0.165 0.307 0.293 0.164 0.165 0.332 0.254 0.164 0.238 0.177
## [23557] 0.164 0.164 0.305 0.254 0.164 0.164 0.313 0.168 0.164 0.164 0.199 0.179
## [23569] 0.164 0.164 0.261 0.177 0.164 0.164 0.258 0.267 0.164 0.164 0.392 0.330
## [23581] 0.164 0.164 0.164 0.284 0.164 0.164 0.331 0.315 0.164 0.164 0.333 0.200
## [23593] 0.164 0.164 0.201 0.323 0.164 0.164 0.202 0.205 0.164 0.164 0.251 0.164
## [23605] 0.164 0.316 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.246 0.164 0.164
## [23617] 0.164 0.164 0.164 0.303 0.164 0.164 0.164 0.279 0.164 0.330 0.163 0.164
## [23629] 0.164 0.183 0.181 0.164 0.164 0.276 0.164 0.164 0.178 0.164 0.280 0.169
## [23641] 0.164 0.164 0.169 0.164 0.162 0.164 0.164 0.211 0.164 0.164 0.186 0.167
## [23653] 0.206 0.169 0.164 0.164 0.179 0.169 0.164 0.164 0.183 0.222 0.165 0.164
## [23665] 0.164 0.164 0.165 0.164 0.163 0.164 0.163 0.164 0.165 0.163 0.165 0.165
## [23677] 0.178 0.165 0.165 0.163 0.165 0.164 0.163 0.165 0.163 0.165 0.165 0.165
## [23689] 0.165 0.164 0.165 0.164 0.164 0.164 0.167 0.164 0.165 0.173 0.163 0.165
## [23701] 0.164 0.179 0.165 0.164 0.164 0.164 0.165 0.186 0.164 0.165 0.164 0.163
## [23713] 0.172 0.165 0.164 0.164 0.164 0.163 0.164 0.164 0.313 0.164 0.164 0.209
## [23725] 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.251 0.216
## [23737] 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.333 0.164
## [23749] 0.164 0.164 0.164 0.164 0.164 0.246 0.164 0.164 0.351 0.164 0.164 0.331
## [23761] 0.332 0.164 0.164 0.164 0.164 0.164 0.164 0.313 0.238 0.164 0.164 0.330
## [23773] 0.199 0.317 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.192 0.164 0.164
## [23785] 0.170 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.170 0.164 0.164
## [23797] 0.164 0.164 0.164 0.164 0.164 0.308 0.164 0.309 0.222 0.199 0.197 0.164
## [23809] 0.309 0.278 0.172 0.164 0.164 0.165 0.337 0.164 0.164 0.165 0.165 0.220
## [23821] 0.164 0.165 0.164 0.165 0.184 0.236 0.164 0.164 0.165 0.194 0.199 0.164
## [23833] 0.163 0.163 0.163 0.165 0.163 0.163 0.163 0.165 0.165 0.165 0.163 0.163
## [23845] 0.165 0.164 0.163 0.165 0.163 0.163 0.165 0.165 0.163 0.165 0.163 0.163
## [23857] 0.165 0.165 0.163 0.165 0.163 0.187 0.165 0.163 0.165 0.163 0.181 0.164
## [23869] 0.163 0.165 0.217 0.164 0.164 0.165 0.163 0.163 0.164 0.164 0.222 0.164
## [23881] 0.403 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.204 0.232 0.211 0.165
## [23893] 0.239 0.175 0.164 0.164 0.165 0.164 0.164 0.164 0.207 0.331 0.164 0.165
## [23905] 0.165 0.163 0.171 0.224 0.165 0.163 0.163 0.324 0.163 0.163 0.163 0.216
## [23917] 0.163 0.163 0.163 0.163 0.258 0.163 0.163 0.163 0.245 0.250 0.163 0.163
## [23929] 0.165 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.165 0.163 0.181
## [23941] 0.163 0.163 0.165 0.248 0.181 0.163 0.165 0.163 0.301 0.163 0.163 0.163
## [23953] 0.316 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.204 0.341 0.163
## [23965] 0.163 0.239 0.164 0.163 0.163 0.163 0.385 0.163 0.163 0.165 0.331 0.163
## [23977] 0.163 0.163 0.163 0.163 0.165 0.163 0.164 0.204 0.163 0.165 0.163 0.345
## [23989] 0.164 0.163 0.163 0.165 0.163 0.181 0.205 0.163 0.163 0.165 0.164 0.236
## [24001] 0.163 0.163 0.165 0.165 0.164 0.333 0.163 0.163 0.163 0.165 0.163 0.280
## [24013] 0.165 0.163 0.165 0.163 0.203 0.163 0.166 0.163 0.163 0.303 0.165 0.163
## [24025] 0.165 0.163 0.252 0.165 0.163 0.163 0.250 0.165 0.163 0.165 0.165 0.164
## [24037] 0.164 0.165 0.163 0.165 0.163 0.164 0.164 0.198 0.165 0.163 0.164 0.164
## [24049] 0.165 0.165 0.163 0.164 0.244 0.163 0.164 0.164 0.165 0.163 0.164 0.165
## [24061] 0.165 0.164 0.189 0.183 0.164 0.332 0.165 0.276 0.164 0.174 0.165 0.163
## [24073] 0.165 0.165 0.164 0.309 0.165 0.165 0.172 0.165 0.164 0.322 0.165 0.164
## [24085] 0.163 0.165 0.164 0.163 0.165 0.165 0.164 0.163 0.164 0.165 0.164 0.176
## [24097] 0.164 0.165 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.165
## [24109] 0.165 0.168 0.165 0.164 0.174 0.164 0.165 0.164 0.165 0.336 0.164 0.165
## [24121] 0.164 0.171 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.179 0.164
## [24133] 0.242 0.164 0.167 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [24145] 0.301 0.164 0.164 0.177 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [24157] 0.164 0.164 0.164 0.164 0.180 0.164 0.164 0.168 0.164 0.199 0.163 0.193
## [24169] 0.175 0.312 0.164 0.164 0.248 0.182 0.237 0.195 0.179 0.164 0.165 0.164
## [24181] 0.165 0.164 0.164 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.165 0.163
## [24193] 0.165 0.164 0.165 0.165 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164
## [24205] 0.164 0.165 0.165 0.165 0.163 0.164 0.165 0.165 0.163 0.164 0.165 0.165
## [24217] 0.164 0.165 0.186 0.163 0.164 0.164 0.252 0.164 0.164 0.222 0.164 0.163
## [24229] 0.164 0.163 0.164 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.164 0.163
## [24241] 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [24253] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.382 0.332 0.164 0.181
## [24265] 0.164 0.164 0.323 0.164 0.163 0.164 0.164 0.237 0.164 0.164 0.164 0.254
## [24277] 0.164 0.351 0.165 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.185 0.165
## [24289] 0.164 0.164 0.165 0.331 0.165 0.165 0.165 0.163 0.165 0.165 0.165 0.163
## [24301] 0.165 0.164 0.165 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.165
## [24313] 0.164 0.165 0.164 0.164 0.165 0.327 0.164 0.165 0.163 0.164 0.165 0.164
## [24325] 0.163 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164
## [24337] 0.164 0.164 0.164 0.164 0.163 0.163 0.164 0.290 0.164 0.333 0.289 0.270
## [24349] 0.192 0.290 0.681 0.293 0.672 0.304 0.698 0.599 0.419 0.449 0.261 0.221
## [24361] 0.182 0.180 0.192 0.240 0.404 0.545 0.364 0.288 0.318 0.173 0.275 0.239
## [24373] 0.195 0.254 0.562 0.331 0.302 0.314 0.311 0.281 0.615 0.190 0.487 0.222
## [24385] 0.568 0.662 0.703 0.164 0.572 0.165 0.516 0.164 0.652 0.873 0.166 0.224
## [24397] 0.177 0.475 0.399 0.690 1.443 0.564 0.509 0.419 0.481 0.612 0.970 0.435
## [24409] 0.599 0.671 0.916 0.675 0.390 0.453 0.792 1.186 0.935 0.726 0.851 0.435
## [24421] 0.629 0.718 1.008 0.454 0.597 0.432 0.373 0.444 0.544 0.653 0.377 0.702
## [24433] 0.398 0.469 0.391 0.331 0.442 0.365 0.362 0.265 0.601 0.522 0.605 0.541
## [24445] 0.534 0.636 0.449 0.439 0.486 0.431 0.454 0.412 0.396 0.581 0.302 0.222
## [24457] 0.300 0.732 0.265 0.614 0.316 0.475 0.251 0.321 0.406 0.591 0.571 0.210
## [24469] 0.381 0.289 0.269 0.291 0.490 0.259 0.274 0.411 0.586 0.382 0.245 0.242
## [24481] 0.364 0.664 0.237 1.527 1.016 0.919 0.768 0.758 0.820 1.663 0.332 0.569
## [24493] 0.666 0.421 1.079 0.777 1.020 1.115 0.298 0.231 0.178 0.258 0.361 0.347
## [24505] 0.565 0.216 0.433 0.297 0.363 0.296 0.294 0.298 0.381 0.327 0.443 0.373
## [24517] 0.560 0.332 0.416 0.424 0.345 0.429 0.276 0.340 0.593 0.374 0.606 0.336
## [24529] 0.488 0.371 0.577 0.258 0.411 0.239 0.272 0.229 0.246 0.261 0.284 0.280
## [24541] 0.179 0.164 0.163 0.164 0.165 0.225 0.185 0.206 0.826 0.951 0.283 0.759
## [24553] 0.340 0.450 0.668 0.239 0.263 0.461 0.330 0.620 0.584 0.300 1.047 1.089
## [24565] 0.895 0.372 0.797 0.392 1.930 0.472 0.203 0.285 0.225 0.221 0.219 0.290
## [24577] 0.502 0.284 0.247 0.259 0.496 0.473 0.254 0.284 0.329 0.338 0.319 0.180
## [24589] 0.164 0.163 0.164 0.165 0.164 0.163 0.443 0.956 0.199 0.304 1.067 0.772
## [24601] 0.890 0.872 1.188 0.307 0.260 0.431 0.300 0.395 0.306 0.538 0.413 0.429
## [24613] 0.371 0.532 0.450 0.511 0.409 0.310 0.528 0.163 0.164 0.162 0.164 0.165
## [24625] 0.164 0.164 0.164 0.164 0.164 0.371 0.163 0.165 0.164 0.163 0.162 0.325
## [24637] 0.280 0.350 0.297 0.284 0.206 0.366 0.424 0.486 0.437 0.386 0.500 0.528
## [24649] 0.588 0.199 0.197 0.331 0.297 0.316 0.421 0.299 1.350 0.224 0.635 0.197
## [24661] 0.269 0.233 1.623 0.291 0.815 0.565 0.395 0.929 0.270 0.238 0.742 0.268
## [24673] 0.214 0.197 0.203 0.233 0.218 0.215 0.458 0.228 0.269 0.341 0.456 0.351
## [24685] 0.227 0.191 0.382 0.205 0.308 0.313 0.239 0.225 0.220 0.168 0.184 0.358
## [24697] 1.794 0.455 0.544 0.365 1.586 0.350 0.453 0.728 1.075 0.937 0.786 0.252
## [24709] 0.273 0.506 0.659 0.306 0.187 0.164 0.163 0.164 0.165 0.171 0.163 0.173
## [24721] 0.181 0.340 0.763 0.272 0.393 0.199 0.223 0.372 0.388 0.448 0.482 0.507
## [24733] 0.543 0.535 0.441 0.592 0.671 0.436 0.525 0.401 0.404 0.376 0.300 0.374
## [24745] 0.368 0.449 0.432 0.340 0.394 0.405 0.360 0.519 0.452 0.516 0.407 0.551
## [24757] 0.373 0.406 0.337 0.282 0.350 0.417 0.326 0.282 0.260 0.409 0.332 0.206
## [24769] 0.205 0.447 0.239 0.612 0.284 0.251 0.355 0.185 0.164 0.163 0.164 0.165
## [24781] 0.164 0.163 0.166 0.179 0.794 0.226 0.489 0.916 0.633 0.551 0.590 0.609
## [24793] 0.658 0.635 0.701 0.470 0.379 0.409 0.680 0.956 0.296 0.269 0.272 0.287
## [24805] 0.347 0.252 0.307 0.259 0.362 0.616 0.396 0.358 0.372 0.293 0.386 0.463
## [24817] 0.463 0.497 0.315 0.400 0.383 0.354 0.351 0.308 0.395 0.342 0.412 0.273
## [24829] 0.493 0.400 0.631 0.203 0.310 0.260 0.358 0.380 0.256 1.395 0.820 0.537
## [24841] 0.818 0.924 0.920 0.340 0.481 0.584 0.359 0.462 0.438 0.995 1.124 0.430
## [24853] 0.656 0.223 0.472 0.270 0.395 0.260 0.266 0.386 0.266 0.294 0.321 0.243
## [24865] 0.293 0.852 0.341 0.261 0.385 0.297 0.369 0.265 0.228 0.397 0.350 0.413
## [24877] 0.413 0.259 0.376 0.198 0.198 0.168 0.165 0.164 0.164 0.165 0.164 0.164
## [24889] 0.164 0.214 0.164 0.480 0.495 0.457 0.507 0.514 0.486 0.314 0.197 0.163
## [24901] 0.165 0.162 0.170 0.172 0.165 0.165 0.166 0.165 0.178 0.176 0.181 0.288
## [24913] 0.230 0.266 0.346 0.425 0.227 0.298 0.274 0.327 0.241 0.457 0.548 0.275
## [24925] 0.587 0.334 0.508 0.438 0.591 0.200 0.479 0.460 0.408 0.364 0.365 0.517
## [24937] 0.433 0.290 0.524 0.192 0.164 0.163 0.164 0.165 0.162 0.163 0.162 0.162
## [24949] 0.164 0.164 0.164 0.164 0.164 0.170 0.168 0.165 0.164 0.173 0.194 0.212
## [24961] 0.303 0.411 0.277 0.359 0.235 0.304 0.195 0.257 0.201 0.234 0.204 0.258
## [24973] 0.246 0.239 0.281 0.255 0.293 0.276 0.407 0.333 0.236 0.241 0.287 0.274
## [24985] 0.360 0.349 0.165 0.165 0.164 0.164 0.164 0.163 0.164 0.162 0.162 0.168
## [24997] 0.178 0.219 0.216 0.204 0.371 0.316 0.459 0.447 0.466 0.226 0.547 0.424
## [25009] 0.441 0.554 0.361 0.165 0.162 0.163 0.175 0.166 0.192 0.166 0.170 0.179
## [25021] 0.174 0.165 0.165 0.171 0.181 0.162 0.164 0.163 0.164 0.165 0.166 0.162
## [25033] 0.163 0.164 0.175 0.171 0.164 0.165 0.164 0.162 0.176 0.254 0.167 0.176
## [25045] 0.240 0.162 0.276 0.162 0.181 0.162 0.177 0.162 0.229 0.162 0.182 0.165
## [25057] 0.162 0.195 0.162 0.208 0.162 0.166 0.176 0.164 0.168 0.197 0.174 0.164
## [25069] 0.163 0.164 0.163 0.165 0.163 0.165 0.163 0.165 0.163 0.165 0.163 0.165
## [25081] 0.163 0.165 0.163 0.165 0.163 0.165 0.163 0.164 0.165 0.164 0.164 0.164
## [25093] 0.167 0.163 0.165 0.162 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164
## [25105] 0.162 0.162 0.162 0.170 0.217 0.279 0.226 0.164 0.164 0.165 0.165 0.164
## [25117] 0.164 0.165 0.164 0.163 0.165 0.163 0.164 0.163 0.165 0.164 0.163 0.165
## [25129] 0.162 0.164 0.163 0.237 0.309 0.217 0.222 0.319 0.251 0.333 0.237 0.256
## [25141] 0.361 0.440 0.213 0.389 0.320 0.250 0.363 0.432 0.256 0.211 0.369 0.424
## [25153] 0.305 0.285 0.319 0.355 0.379 0.256 0.231 0.216 0.258 0.393 0.363 0.401
## [25165] 0.383 0.406 0.382 0.373 0.337 0.169 0.244 0.178 0.172 0.180 0.227 0.203
## [25177] 0.202 0.201 0.201 0.210 0.249 0.189 0.194 0.224 0.237 0.259 0.212 0.163
## [25189] 0.164 0.163 0.190 0.166 0.196 0.167 0.200 0.166 0.200 0.166 0.204 0.165
## [25201] 0.192 0.166 0.173 0.164 0.164 0.165 0.167 0.165 0.172 0.208 0.175 0.166
## [25213] 0.174 0.175 0.178 0.188 0.197 0.191 0.198 0.198 0.175 0.181 0.196 0.166
## [25225] 0.248 0.285 0.226 0.177 0.180 0.195 0.332 0.299 0.241 0.278 0.287 0.273
## [25237] 0.362 0.335 0.267 0.191 0.328 0.211 0.349 0.347 0.475 0.177 0.206 0.264
## [25249] 0.261 0.246 0.370 0.257 0.365 0.361 0.403 0.306 0.329 0.405 0.413 0.386
## [25261] 0.369 0.352 0.285 0.374 0.366 0.382 0.199 0.226 0.189 0.196 0.219 0.212
## [25273] 0.217 0.210 0.197 0.226 0.213 0.221 0.204 0.229 0.240 0.208 0.191 0.213
## [25285] 0.256 0.441 0.231 0.210 0.254 0.400 0.355 0.195 0.265 0.207 0.189 0.202
## [25297] 0.220 0.208 0.200 0.193 0.196 0.181 0.178 0.178 0.185 0.212 0.177 0.175
## [25309] 0.200 0.190 0.210 0.206 0.193 0.178 0.203 0.190 0.177 0.200 0.196 0.238
## [25321] 0.164 0.228 0.180 0.203 0.191 0.193 0.205 0.207 0.182 0.209 0.195 0.217
## [25333] 0.204 0.198 0.218 0.211 0.189 0.171 0.341 0.342 0.316 0.208 0.179 0.196
## [25345] 0.187 0.256 0.246 0.257 0.316 0.256 0.243 0.317 0.314 0.325 0.229 0.165
## [25357] 0.306 0.164 0.306 0.164 0.322 0.166 0.164 0.166 0.164 0.167 0.164 0.164
## [25369] 0.165 0.166 0.164 0.164 0.164 0.166 0.164 0.164 0.164 0.164 0.164 0.164
## [25381] 0.169 0.164 0.164 0.250 0.164 0.188 0.165 0.164 0.164 0.197 0.164 0.164
## [25393] 0.164 0.164 0.179 0.175 0.167 0.175 0.166 0.185 0.173 0.192 0.211 0.205
## [25405] 0.232 0.526 0.192 0.201 0.218 0.202 0.191 0.181 0.164 0.167 0.168 0.165
## [25417] 0.168 0.169 0.168 0.173 0.168 0.183 0.166 0.187 0.169 0.187 0.169 0.190
## [25429] 0.188 0.180 0.205 0.213 0.203 0.197 0.175 0.192 0.205 0.177 0.189 0.191
## [25441] 0.181 0.187 0.183 0.170 0.172 0.173 0.175 0.182 0.193 0.193 0.171 0.165
## [25453] 0.180 0.163 0.201 0.193 0.193 0.165 0.185 0.179 0.221 0.213 0.180 0.218
## [25465] 0.202 0.193 0.189 0.164 0.164 0.164 0.164 0.316 0.354 0.195 0.278 0.244
## [25477] 0.390 0.245 0.207 0.193 0.224 0.466 0.441 0.193 0.214 0.228 0.220 0.248
## [25489] 0.305 0.170 0.165 0.163 0.199 0.168 0.168 0.168 0.167 0.166 0.232 0.166
## [25501] 0.166 0.164 0.169 0.165 0.167 0.164 0.166 0.165 0.164 0.165 0.171 0.311
## [25513] 0.164 0.168 0.165 0.167 0.173 0.190 0.174 0.163 0.171 0.211 0.164 0.164
## [25525] 0.265 0.364 0.165 0.165 0.163 0.267 0.165 0.162 0.163 0.162 0.162 0.164
## [25537] 0.165 0.162 0.164 0.163 0.164 0.164 0.162 0.165 0.162 0.164 0.163 0.181
## [25549] 0.167 0.262 0.228 0.297 0.305 0.491 0.246 0.188 0.163 0.163 0.162 0.162
## [25561] 0.164 0.162 0.163 0.164 0.163 0.162 0.165 0.192 0.177 0.232 0.164 0.164
## [25573] 0.340 0.197 0.167 0.164 0.165 0.169 0.169 0.171 0.182 0.180 0.178 0.175
## [25585] 0.181 0.178 0.181 0.186 0.205 0.232 0.192 0.190 0.186 0.165 0.279 0.165
## [25597] 0.165 0.164 0.165 0.163 0.328 0.187 0.182 0.175 0.183 0.172 0.169 0.167
## [25609] 0.168 0.186 0.170 0.170getFitted(Final7.1) #predictions of the model for all points
x1 = getSimulations(Final7.1, nsim = 5, type = "refit") #extract simulations from the model
getRefit(Final7.1, x1[[1]]) #model with simulated data## Formula:
## KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 |
## Transmitter) + (1 | File)
## Data: newData
## AIC BIC logLik df.resid
## -75245.12 -75163.61 37632.56 25602
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## Transmitter (Intercept) 0.2377
## File (Intercept) 0.1141
##
## Number of obs: 25612 / Conditional model: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.061
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) LengthStd Habitatdemersal
## -1.70265 0.33413 -0.19220
## Habitatpelagic-neritic ComImportmedium ComImportminor
## 0.36181 -0.23743 -0.03352
## MonitArea_km2
## 0.02185getRefit(Final7.1, getObservedResponse(Final7.1)) #model with real data## Formula:
## KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 |
## Transmitter) + (1 | File)
## Data: newData
## AIC BIC logLik df.resid
## -76373.66 -76292.16 38196.83 25602
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## Transmitter (Intercept) 0.2429
## File (Intercept) 0.1662
##
## Number of obs: 25612 / Conditional model: Transmitter, 850; File, 48
##
## Dispersion estimate for Gamma family (sigma^2): 0.0607
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) LengthStd Habitatdemersal
## -1.71392 0.30948 -0.17781
## Habitatpelagic-neritic ComImportmedium ComImportminor
## 0.35483 -0.13767 -0.09744
## MonitArea_km2
## 0.02343#create a dataframe with the simulated data and the true data
df1 <- data.frame(x1$sim_1, x1$sim_2, x1$sim_3, week_kuds$KUD50, week_kuds$LengthStd, week_kuds$Habitat, week_kuds$ComImport)
#plot KUD50 (real and simulated) against LengthStd
grid.arrange(ggplot(data= df1, aes(x = week_kuds.LengthStd, y=week_kuds.KUD50)) + geom_point(col="black") + scale_y_continuous(limits = c(0, 4)) + xlab("Length Std") + ylab("real KUD50"), ggplot(data= df1, aes(x = week_kuds.LengthStd, y=x1.sim_1)) + geom_point(col="green3") + scale_y_continuous(limits = c(0, 4)) + xlab("Length Std") + ylab("KUD50 simulation 1"), ggplot(data= df1, aes(x = week_kuds.LengthStd, y=x1.sim_2)) + geom_point(col="green") + scale_y_continuous(limits = c(0, 4)) + xlab("Length Std") + ylab("KUD50 simulation 2"), ggplot(data= df1, aes(x = week_kuds.LengthStd, y=x1.sim_3)) + geom_point(col="green4") + scale_y_continuous(limits = c(0, 4)) + xlab("Length Std") + ylab("KUD50 simulation 3"))
#plot KUD50 (real and simulated) against Commercial importance
grid.arrange(ggplot(data = df1, aes(x = week_kuds.ComImport, y=week_kuds.KUD50)) +
geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 4)) + xlab("Commercial Importance") + ylab("real KUD50"), ggplot(data = df1, aes(x = week_kuds.ComImport, y=x1.sim_1)) +
geom_boxplot(fill = "green3") + scale_y_continuous(limits = c(0, 4)) + xlab("Commercial Importance") + ylab("KUD50 simulation 1"), ggplot(data = df1, aes(x = week_kuds.ComImport, y=x1.sim_2)) +
geom_boxplot(fill = "green") + scale_y_continuous(limits = c(0, 4)) + xlab("Commercial Importance") + ylab("KUD50 simulation 2"), ggplot(data = df1, aes(x = week_kuds.ComImport, y=x1.sim_3)) + geom_boxplot(fill = "green4") + scale_y_continuous(limits = c(0, 4)) + xlab("Commercial Importance") + ylab("KUD50 simulation 3"), ncol = 4)
#plot KUD50 (real and simulated) against Habitat
grid.arrange(ggplot(data = df1, aes(x = week_kuds.Habitat, y=week_kuds.KUD50)) +
geom_boxplot(fill = "black") + scale_y_continuous(limits = c(0, 4)) + xlab("Habitat") + ylab("real KUD50"), ggplot(data = df1, aes(x = week_kuds.Habitat, y=x1.sim_1)) +
geom_boxplot(fill = "green3") + scale_y_continuous(limits = c(0, 4)) + xlab("Habitat") + ylab("KUD50 simulation 1"), ggplot(data = df1, aes(x = week_kuds.Habitat, y=x1.sim_2)) +
geom_boxplot(fill = "green") + scale_y_continuous(limits = c(0, 4)) + xlab("Habitat") + ylab("KUD50 simulation 2"), ggplot(data = df1, aes(x = week_kuds.Habitat, y=x1.sim_3)) + geom_boxplot(fill = "green4") + scale_y_continuous(limits = c(0, 4)) + xlab("Habitat") + ylab("KUD50 simulation 3"), ncol = 4)
The normality and homoscedasticity assumptions aren’t met. However, given the large dataset, this violation may not be a big problem. To investigate this, we simulated the response values and compared with the real ones. After observing the results, we concluded that the patterns were similar and the model can be said to be well adjusted.